Garuda - Garba Rujukan Digital

p-Index From 2019 - 2024

1.745

P-Index

This Author published in this journals

All Journal TEKNIK INFORMATIKA CESS (Journal of Computer Engineering, System and Science) AL-ISHLAH: Jurnal Pendidikan Jurnal Nasional Komputasi dan Teknologi Informasi Krea-TIF: Jurnal Teknik Informatika Journal of Applied Engineering and Technological Science (JAETS) Jurnal Tekinkom (Teknik Informasi dan Komputer) Jurnal Computer Science and Information Technology (CoSciTech) Jurnal Teknoif Teknik Informatika Institut Teknologi Padang

Elin Haerani

Universitas Islam Negeri Sultan Syarif Kasim Riau

Author-ID : 2583964

Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Social Sciences

Published : 12 Documents Claim Missing Document

Claim Missing Document

Articles

1 2

PENGEMBANGAN SISTEM PENDUKUNG KEPUTUSAN PENENTUAN KONSENTRASI BIDANG KEAHLIAN MAHASISWA DENGAN INTEREST INVENTORY Elin Haerani; Kasman Rukun; Fahmi Rizal
JURNAL TEKNIK INFORMATIKA Vol 13, No 1 (2020): JURNAL TEKNIK INFORMATIKA
Publisher : Department of Informatics, Universitas Islam Negeri Syarif Hidayatullah

Universities are designed to prepare graduates who are ready to enter the workforce and are able to develop a professional attitude. Educational institutions such as the University need a form of decisions in determining the right concentration for students, so that the learning process can be achieved well. The decision is very influential on the process of handling the choice of alternative concentration, choosing an appropriate concentration of interest will also have an impact on the research focus for the final assignment of students. This research develops student concentration selection system in Electrical. Currently the concentration determination system is based only on academic assessment alone, regardless of student interest, so that it can impact on student learning outcomes. The system was developed by combining academic judgment and interest inventory with three criteria, ie, interest tests using interest inventory, prerequisite concentration course grades, and GPA. The system is built using an intelligent system model that is Fuzzy Multiple Attribute Decision Making (FMADM), which helps the Department in the selection process and helps the process of career guidance on students. With this selection system, the Department can be provide the most suitable concentration decisions with interest in student.

Klasifikasi Berita Menggunakan Metode Support Vector Machine Robbi Nanda; Elin Haerani; Siska Kurnia Gusti; Siti Ramadhani
Jurnal Nasional Komputasi dan Teknologi Informasi (JNKTI) Vol 5, No 2 (2022): April 2022
Publisher : Program Studi Teknik Informatika, Fakultas Teknik. Universitas Serambi Mekkah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32672/jnkti.v5i2.4193

Abstrak - Berita adalah sebuah informasi mengenai peristiwa yang terjadi di suatu lokasi yang bisa disajikan dalam bentuk teks maupun visual. Berita bisa ditemukan di berbagai portal berita dan media cetak. Umumnya setiap berita dikelompokan berdasarkan kategori umum seperti ekonomi, politik, olahraga, dll. Permasalahan yang muncul adalah bagaimana cara untuk melakukan pengelompokan pada data berita yang biasanya berjumlah hingga ribuan karakter kedalam kategori yang lebih spesifik. Permasalah ini dapat diselesaikan dengan cara menerapkan text mining dengan memanfatakan algoritma klasifikasi untuk mendapatkan sebuah model fungsi yang merepresentasikan tiap kategori berita. Salah satu algoritma klasifikasi yang cukup tangguh untuk melakukan proses klasifikasi teks adalah Support Vector Machine. Penelitian ini menggunakan 510 data berita dengan batasan klasifikasi 3 kategori berita. Algoritma SVM mendapatkan hasil akurasi tertinggi di 88% untuk nilai parameter C =1, kernel Linear dengan pembagian data uji dan data latih sebesar 90% dan 10 %.Kata kunci : Berita, Klasifikasi, Support Vector Machine, Text Mining Abstract - News is information about events that occur in a location that can be presented in text or visual form. News can be found on various news portals and print media.Generally each news is grouped by general categories such as economics, politics, sports, etc. The problem is how to group news data into more specific categories.This problem can be solved by applying text mining using the classification algorithm to obtain a function model that represents each news category. One of the classification algorithms that is strong enough to do the text classification process is the Support Vector Machine. This study uses 510 news sample with a classification limit of 3 news categories. The SVM algorithm gets the highest accuracy at 88% for the parameter value C = 1, and Linear kernel with the distribution of test data and training data is 90% and 10%.Keywords : Classification, News, Support Vector Machine, Text Mining

Klasifikasi Berita Menggunakan Algoritma C4.5 Yayuk Wulandari; Elin Haerani; Siska Kurnia Gusti; Siti Ramadhani
Jurnal Nasional Komputasi dan Teknologi Informasi (JNKTI) Vol 5, No 2 (2022): April 2022
Publisher : Program Studi Teknik Informatika, Fakultas Teknik. Universitas Serambi Mekkah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32672/jnkti.v5i2.4194

Abstrak - Perkembangan zaman mengalami kemajuan yang sangat pesat, saat ini penyebaran berita yang paling populer adalah melalui internet. Berita yang disajikan di situs berita online biasanya hanya dalam kategori umum, sehingga ketika pembaca ingin mendapatkan kategori berita yang lebih spesifik harus dilakukan secara manual yang tentunya menjadi kegiatan yang cukup merepotkan. Hal ini juga dialami oleh Badan Pusat Statistik Provinsi Riau yang kesulitan dalam mencari dan mengklasifikasikan berita tentang Provinsi Riau. Dalam hal ini penerapan klasifikasi otomatis dirasa sangat diperlukan. Penelitian ini menggunakan metode klasifikasi C4.5 dengan 510 data berita yang akan diklasifikasikan menjadi 3 kategori yaitu demokrasi, kemiskinan dan ketenagakerjaan. Proses klasifikasi berita dalam penelitian ini meliputi: pengumpulan data, pelabelan manual, teks preprocessing, pembobotan kata, dan metode klasifikasi C4.5. Berdasarkan penelitian yang dilakukan, hasil uji akurasi adalah 84% dengan menggunakan pembagian data 90%:10%. Dapat disimpulkan bahwa metode C4.5 cocok digunakan dalam klasifikasi berita.Kata kunci: Badan Pusat Statistik, Berita, C4.5, Klasifikasi. Abstract - The development of the times has progressed very rapidly, currently the most popular spread of news is through the internet. The news presented on online news sites is usually only in general categories, so when readers want to get a more specific category of news, it must be done manually, which of course will be a bit of a hassle. This is also experienced by the social sector of the Badan Pusat Statistik of Riau, which has difficulty finding and classifying news about Riau Province. In this case the application of automatic classification is felt to be very necessary. This study uses the C4.5 classification method with 510 news data which will be classified into 3 categories, namely democracy, poverty and employment. The news classification process in this study includes: data collection, manual labeling, preprocessing text, word weighting, and C4.5 classification method. Based on the research conducted, the results of the accuracy test were 84% using 90%:10% data sharing. It can be concluded that the C4.5 method is suitable for use in news classification.Keywords : Badan Pusat Statistik, C4.5, Classification, News.

Rancang Bangun Sistem Pendukung Keputusan Pendistribusian Zakat Menggunakan Fuzzy Multiple Attribute Decission Making (FMADM) Dan Simple Additive Weighting (SAW) Pada Baznas Kota Pekanbaru Elin Haerani; Ramdaril
Jurnal Teknoif Teknik Informatika Institut Teknologi Padang Vol 3 No 2 (2015): JURNAL TEKNOIF ITP
Publisher : ITP Press

Baznas Pekanbaru mendistribusikan zakat untuk para mustahik dilakukan secara periodik yaitu sekali dalam tiga (3) bulan. Pendistribusian zakat dilakukan melalui program Pekanbaru Cerdas, Pekanbaru Makmur, dan Pekanbaru Sehat. Cara pendistribusian zakat dilakukan dengan mempertimbangkan kriteria penerima zakat yang dihitung dengan cara konvensional oleh para panitia Baznas kota Pekanbaru. Hal ini memungkinkan terjadinya kekeliruan dalam penghitungan dan pertimbangan keputusan.. Oleh karena itu diperlukan sebuah aplikasi sistem pendukung keputusan dalam rangka memberikan dukungan keputusan pendistribusian zakat berdasarkan kriteria-kriteria yang berhak menerima zakat menurut ketentuan Baznas Pekanbaru sehingga pendistribusian zakat sampai kepada orang yang benar-benar berhak. Metode yang digunakan adalah metode SAW (Simple Additive Weighting) yang digunakan untuk mencari bobot penjumlahan pada setiap kriteria yang dimiliki mustahik. Proses yang terjadi pada sistem baru ini adalah mustahik mengajukan permohonan penerimaan zakat, selanjutnya pihak Baznas melakukan survey terhadap mustahik yang bersangkutan. Setelah didapatkan, kemudian hasil survey tersebut diinputkan ke sistem pendukung keputusan. Selanjutnya sistem akan melakukan pengolahan terhadap data masukan yang diberikan sehingga menghasilkan keluaran data berupa mustahik yang memiliki nilai V tertinggi yang otomatis menjadi mustahik yang direkomendasikan untuk mendapatkan zakat sesuai dengan program kerja yang ada. Baznas Pekanbaru of distribution of alms to mustahik do periodically is once in three (3) months. Zakat distribution do through the program Intelligent Pekanbaru, Pekanbaru Prosperous and Healthy Pekanbaru. Manner of distributing zakat is done by considering the criteria for recipients are calculated in the conventional manner by the committee Baznas city of Pekanbaru. This allows errors in the calculation and consideration of the decision. Therefore we need an application of decision support systems in order to provide decision support distribution of zakat based criteria are eligible to receive zakat according to the provisions Baznas Pekanbaru so that the distribution of alms to the people who actually entitled. The method used is the method of SAW (Simple Additive weighting) is used to find the sum of the weights of each criterion owned mustahik. The process that occurs in this new system is mustahik apply for zakat, then the Baznas surveyed mustahik concerned. Once obtained, then the results of the survey is entered into the decision support system. Then the system will perform the processing to the data given input to produce output data in the form mustahik which has the highest value V automatically be mustahik recommended to receive alms in accordance with the existing work program

Penerapan Data Mining Dalam Mencari Pola Asosiasi Data Tracer Study Menggunakan Equivalence Class Transformation (ECLAT) Khairul Amri; Alwis Nazir; Elin Haerani; Muhammad Affandes; Reski Mai Candra; Amany Akhyar
Jurnal Nasional Komputasi dan Teknologi Informasi (JNKTI) Vol 5, No 3 (2022): Juni 2022
Publisher : Program Studi Teknik Informatika, Fakultas Teknik. Universitas Serambi Mekkah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32672/jnkti.v5i3.4408

Abstrak - Tracer study adalah sebuah pendekatan yang diterapkan universitas untuk memperoleh informasi tentang kemungkinan kelemahan dalam proses pendidikan dan proses pembelajaran yang menjadi dasar perencanaan aktivitas untuk penyempurnaan di masa mendatang. Pada Universitas Islam Negeri Sultan Syarif Kasim Riau belum pernah ada tracer study yang komprehensif dan terstruktur dalam ruang lingkup universitas. Tracer study yang dilakukan hanya dalam lingkup program studi dan hanya dilaksanakan menjelang proses akreditasi prodi. Tidak ada tracer study yang rutin dilakukan di tingkat program studi dan universitas setiap tahunnya. Pada penelitian ini akan berfokus kepada penerapan data mining untuk mencari pola asosiasi pada data tracer study menggunakan Equivalence Class Transformation (ECLAT). Dari hasil penelitian terdapat 4 pola yang memenuhi support 13% dan confidence 80% dengan pengujian lift rasio 1. Pola tersebut diantaranya Jika ipk antara 3 – 3,5 dan gaji pertama dibawah 3 juta dan laki-laki maka status kelulusan tidak tepat waktu dan “masa tunggu mendapatkan pekerjaan pertama kurang dari 6 bulan” dengan support 17% dan confidence 84%. Jika ipk antara 3 – 3,5 dan perempuan maka “masa tunggu mendapatkan pekerjaan pertama kurang dari 6 bulan” dan hubungan pekerjaan dengan jurusan sesuai dengan support 14 % dan confidence 100%.Kata kunci: Tracer Study, Data Mining, Asosiasi, Equivalence Class Transformation, Eclat Abstract - Tracer study is an approach applied by universities to obtain important information in the education and learning process which is the basis for planning activities for future improvement. At UIN SUSKA University there has never been a comprehensive and structured tracer study within the scope of the university. There is no routine tracer study conducted at the study program and university level every year. This research will focus on the application of data mining to find association rules in tracer study data using ECLAT. From the research results, there are 4 patterns that meet the support of 13% and 80% confidence with a lift ratio test 1. The patterns include If the ipk is between 3 - 3.5 and the first salary is below 3 million and male then the graduation status is not on time and the waiting period to get the first job is less than 6 months with 17% support and 84% confidence. If the ipk is between 3 - 3.5 and female, then the waiting period to get the first job is less than 6 months and the job relationship with the major is in accordance with the support of 14% and confidence 100%.Keywords: Tracer Study, Data Mining, Asosiasi, Equivalence Class Transformation, Eclat

DESAIN ARSITEKTUR DATA WAREHOUSE PADA DATA TRANSAKSI PENJUALAN ROTTE BAKERY Devi Julisca Sari; Siska Kurnia Gusti; Alwis Nazir; Elin Haerani; Fadhilah Syafria
Jurnal Tekinkom (Teknik Informasi dan Komputer) Vol 5 No 2 (2022)
Publisher : Politeknik Bisnis Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37600/tekinkom.v5i2.605

The increasingly fierce competition between competitors requires companies to be able to compete and maintain their existence in order to continue to grow, for that utilizing information technology such as data warehouses will play a large enough role, because optimal data processing will produce quality information in supporting companies to take appropriate policies. as well as increasing the productivity and effectiveness of the company's performance. The application of the data warehouse can be started by making an architectural design that will be made, for that the researcher aims to provide recommendations for the design of the data warehouse architecture on the sales transaction data of Rotte Bakery by applying the Nine Steps Kimball method. The final result of this research is the application of the Nine Steps Kimball method and the integration of transaction data through the ETL process (extract, transform, load) successfully produces data stored in the data warehouse only the data that is needed and has been uninformed, so that data processing only takes a long time. shorter time in supporting appropriate policy making and achieving business strategies in order to be able to keep pace with the business competition

Analisis sentimen larangan penggunaan obat sirup menggunakan algoritma naive bayes classifier Fitri Wulandari; Elin Haerani; Muhammad Fikry; Elvia Budianita
Computer Science and Information Technology Vol 4 No 1 (2023): Jurnal Computer Science and Information Technology (CoSciTech)
Publisher : Universitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/coscitech.v4i1.4781

The Indonesian government made a policy to stop consuming syrup as a form of prevention against acute kidney failure, which affects many people in Indonesia. However, the policy has caused a lot of comments from the public. These public comments can be found on YouTube, because YouTube has a large data source opportunity to be used as a research material. These comments can be processed directly without using a machine, but it is less effective and efficient. Thus, the comments are processed using machine learning methods. Based on the earlier research, the naive bayes classifier algorithm tends to be simple and easy to use. In addition, this algorithm also has a high accuracy. The amount of data used in this study is 1000 YouTube comment data related to videos regarding the policy of prohibiting the use of syrup medicine, the comments are divided into 2 category, which are positive class and negative class. The results of labeling 1000 comments obtained 704 negative comments and 296 positive comments. Based on the experiments conducted using python programming language, the highest accuracy was obtained at 74% in 70:30 data split. Furthermore, in the balanced dataset (296 positive and 296 negative comments), the highest accuracy was obtained at 64.70% with in 80:20 data split. These results represent that the naive bayes classifier algorithm is good enough at sentiment analysis about the policy of prohibiting the use of syrup drugs.

Pengelompokan pembagian zakat dengan menggunakan metode clustering k-means Alvin Alvin Anzaz Islami; Elin Haerani; Novriyanto; Alwis Nazir
Computer Science and Information Technology Vol 4 No 1 (2023): Jurnal Computer Science and Information Technology (CoSciTech)
Publisher : Universitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/coscitech.v4i1.4804

Zakat merupakan ibadah yang menyangkut harta benda. Zakat juga termasuk rukun islam yang ke empat yang memiliki tujuan menyucikan harta bagi setiap muslim dengan cara menyisihkan sebagian harta kekayaannya, jika telah mencapai waktu dan besaran jumlahnya diberikan kepada orang yang berhak menerimanya. Pengumpulan dan penyaluran zakat biasanya ditangani oleh Badan Amil Zakat (BAZ) yang ada disetiap wilayah Indonesia, salah satunya di Pekanbaru. Sesuai dengan peraturan ada dua tahap yang dilakukan dalam memberikan bantuan kepada para mustahik yaitu melakukan wawancara dan observasi lapangan, kemudian menentukan nominal bantuan yang diberikan dengan kategori Mustahik penerima bantuan zakat 1, zakat 2, dan zakat 3. Masalah yang sering dijumpai dalam penentuan calon penerima bantuan adalah cara dalam pemilihan Mustahik yang masih menggunakan cara manual, sehingga sering menimbulkan masalah seperti lamanya proses pemilihan dan terjadinya salah hitung sehingga hasil seleksi Mustahik menjadi kurang akurat. Untuk itu, perlu dibuat suatu analisis yang dapat mengolah data menjadi informasi. Data mining ialah proses untuk mengolah data menjadi suatu informasi dengan teknik statistik, AI, dan machine learning. Ada banyak metode dalam data mining. Pada penelitian ini menggunakan algoritma k-means clustering dan untuk pengujian menggunakan Davies Bouldin Index. berdasarkan pengujian menggunakan davies bouldin index (DBI) klaster 4 merupakan klaster terbaik dengan nilai 0.671, dimana jika nilainya semakin rendah maka akan semakin baik klaster tersebut

Clustering Keluarga Miskin Desa Bina Baru dengan Metode K-Medoids Felina Amelia; Iwan Iskandar; Siska Kurnia Gusti; Elin Haerani; Yusra Yusra
Krea-TIF: Jurnal Teknik Informatika Vol 11 No 1 (2023)
Publisher : Fakultas Teknik dan Sains, Universitas Ibn Khaldun Bogor

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32832/krea-tif.v11i1.14104

Kemiskinan di Indonesia terjadi di berbagai daerah, mulai pedesaan hingga perkotaan memiliki permasalahan kemiskinan masing – masing. Masalah kemiskinan juga dialami oleh Desa Bina Baru. Desa Bina Baru yang memiliki jumlah penduduk sebanyak 5.760 jiwa dengan total 1.742 keluarga, yang tersebar dalam 30 Rukun Tetangga (RT) dan 8 Rukun Warga (RW). Upaya dalam penurunan angka kemiskinan dapat dilakukan dengan berbagai cara, mulai pembangunan yang merata, penyaluran bantuan yang tepat sasaran, pemberian kebijakan yang tepat, dan lain sebagainya. Pengelompokan kemiskinan menjadikan salah satu upaya untuk menurunkan angka kemiskinan agar dapat memberikan informasi kepada pemerintahan daerah dalam memberikan kebijakan yang lebih tepat guna. Clustering merupakan teknik data mining yang bertujuan untuk mengelompokkan objek-objek data menjadi beberapa Cluster. Pada penelitian ini pengelompokkan dilakukan dengan teknik pengolahan data mining dengan algoritme K-Medoids dari data Desa Bina Baru tahun 2020 berjumlah 1.005. Hasil perbandingan perhitungan untuk Cluster 1 (kaya) sebanyak 527 penduduk, Cluster 2 (menengah) sebanyak 248 penduduk, dan Cluster 3 (miskin) sebanyak 225 penduduk, Hasil evaluasi dari algoritme k-Medoids adalah 0,991 yang menunjukan cluster yang dibentuk memberikan pengelompokan informasi yang baik. Hasil pengelompokan ini dapat dijadikan acuan untuk informasi kelompok keluarga miskin yang diperlukan pemerintah agar bantuan yang diberikan tepat sasaran.

Classification Academic Data using Machine Learning for Decision Making Process Elin Haerani; Fadhilah Syafria; Fitra Lestari; Novriyanto Novriyanto; Ismail Marzuki
Journal of Applied Engineering and Technological Science (JAETS) Vol. 4 No. 2 (2023): Journal of Applied Engineering and Technological Science (JAETS)
Publisher : Yayasan Riset dan Pengembangan Intelektual (YRPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37385/jaets.v4i2.1983

One of the qualities of higher education is determined by the success rate of student learning. Assessment of student success rates is based on student graduation on time. Sultan Syarif Kasim State Islamic University Riau is one of the state universities in Riau, with a total of 30,000 students. Of all the active students, there are some who are not. Students who are not active in this case will affect the timeliness of their graduation. The university always evaluates the performance of its students to find out information related to the factors that cause students to become inactive so that they are more likely to drop out and what data affect students being able to graduate on time. The evaluation results are stored in an academic database so that the data can later be used as supporting data when making decisions by the university. This research used data science concepts to explore and extract data sets from databases to find models or patterns, as well as new insights that can be used as tools for decision-making. After the data was explored, machine learning concepts were used to identify and classify the data. The method used was the Decision Tree Method. The results of the study found that these two concepts can provide the expected results. Based on the test results, it is known that the attribute that influences the success of student studies is the grade point average (GPA), where the accuracy of the maximum recognition rate is 88.19%. Keywords : Data science; Decision Tree; Graduate on Time; Machine Learning;

Co-Authors Abu Anwar Alvin Alvin Anzaz Islami Alwis Nazir Amany Akhyar Damayanti, Elok Devi Julisca Sari Elvia Budianita Fadhilah Syafria Fahmi Rizal Felina Amelia Fitra Lestari Ismail Marzuki Iwan Iskandar Jasril Jasril Kasman Rukun Khairul Amri Lola Oktavia Muhammad Affandes Muhammad Fikry Novriyanto Novriyanto Ramdaril Ramdaril Ramdaril Reski Mai Candra Robbi Nanda Said Nanda Saputra Samsi Hasan Siska Kurnia Gusti Siti Ramadhani Yayuk Wulandari Yusra, Yusra

Title

Found 12 Documents
Search

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Title Search

Found 12 Documents Search

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Title

Found 12 Documents
Search