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All Journal Khazanah Informatika: Jurnal Ilmu Komputer dan Informatika KLIK (Kumpulan jurnaL Ilmu Komputer) (e-Journal) Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Teknik Komputer Journal of Innovation Information Technology and Application (JINITA) INISTA Journal of Dinda : Data Science, Information Technology, and Data Analytics Jurnal Saintekom : Sains, Teknologi, Komputer dan Manajemen

Amalia Beladinna Arifa

Institut Teknologi Telkom Purwokerto

Author-ID : 2489349

Computer Science & IT Decision Sciences, Operations Research & Management Engineering

Published : 11 Documents Claim Missing Document

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Information Retrieval on Exam Questions Referring to the Learning Plan Document Using Vector Space Model Amalia Beladinna Arifa; Gita Fadila Fitriana; Ananda Rifkiy Hasan
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 5 No 1 (2021): Februari 2021
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

One way to find out the quality of exam questions is by looking at the rules for writing exam questions made based on the subject or discussion contained in the learning plan document. Therefore, the exam questions that are arranged must be adjusted to the main material in each subject learning achievement. This study discusses the implementation of the concept in information retrieval systems using the Vector Space Model method. The Vector Space Model method has an advantage in query matching because it is able to match only part of the query with existing documents. In addition, the Vector Space Model method is also easy to adapt by adjusting parameters, including weighting parameters. The weighting calculation for each term that appears in the document uses TF-IDF. The purpose of this study is to design an information retrieval system to find the suitability of the exam question query with the subject contained in the learning plan document. The suitability is sorted based on the similarity value of the calculation results, from the largest value to the smallest value in the form of a percentage.

Sistem Pendukung Keputusan Kelompok untuk Penentuan Usulan Lokasi Pendirian Minimarket Amalia Beladinna Arifa; Herdiesel Santoso
JURNAL TEKNIK KOMPUTER Vol 6, No 2 (2020): JTK-Periode Juli 2020
Publisher : Universitas Bina Sarana Informatika

Pasar modern merupakan tempat menjual berbagai barang kebutuhan rumah tangga dimana penjualannya dilakukan dengan cara konsumen mengambil sendiri barang yang dibutuhkan dan membayarnya ke kasir. Dalam kurun waktu 10 tahun terakhir, salah satu jenis pasar modern yang memiliki laju pertumbuhan omset adalah minimarket. Pada lokasi yang tepat, sebuah minimarket akan lebih sukses dibandingkan dengan minimarket lain yang memiliki lokasi kurang strategis meskipun keduanya menjual barang yang sama. Dalam menentukan lokasi pendirian minimarket yang baru dibutuhkan parameter yang perlu diperhatikan oleh para pengambil keputusan. Untuk menentukan lokasi pendirian minimarket akan sulit dilakukan jika dikerjakan secara manual, mengingat setiap pengambil keputusan memiliki preferensi sendiri yang dapat menyebabkan tingginya faktor subjektifitas. Sistem Pendukung Keputusan Kelompok (SPKK) dapat menjadi alternatif untuk membantu mengambil keputusan sekaligus sebagai alat dalam menganalisis informasi yang dibutuhkan. Pemodelan SPKK yang dikembangkan dalam penelitian ini menggunakan penggabungan beberapa metode, yaitu AHP, SAW dan Borda. Berdasarkan hasil akhir dari Sistem Pendukung Keputusan Kelompok yaitu berupa perangkingan dari kelima nilai parameter (harga tanah, jumlah penduduk, volume belanja, frekuensi belanja, dan pendapatan) terhadap alternatif lokasi pendirian minimarket. Alternatif lokasi yang memiliki hasil tertinggi yang dijadikan sebagai lokasi usulan bagi para pengambil keputusan dalam mendirikan minimarket baru.

Penerapan Teknologi Video 360 Derajat Berbasis Virtual Reality Menggunakan Google Cardboard Sebagai Media Alternatif Pengenalan Kampus Institut Teknologi Telkom Purwokerto Vico Meylana Eka Putra; Novian Adi Prasetyo; Amalia Beladinna Arifa
Journal of INISTA Vol 4 No 1 (2021): November 2021
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/inista.v4i1.398

Institut Teknologi Telkom Purwokerto (ITTP) merupakan perguruan tinggi swasta yang dikelola oleh Yayasan Pendidikan Telkom dan satu-satunya institut yang berada di Jawa Tengah. Perguruan tinggi yang berfokus pada pengembangan ilmu pengetahuan berbasis teknologi informasi membuat daya tarik tersendiri bagi mahasiswa baru yang melanjutkan studinya pada kampus ini, hal tersebut terbukti dengan bertambahnya jumlah mahasiswa baru yang mendaftar setiap tahunnya. Sebagai civitas akademika tentunya para mahasiswa baru diharapkan mengetahui segala informasi fasilitas sarana dan prasarana didalam lingkungan kampus ITTelkom Purwokerto, terutama seperti ruangan dan fasilitas apa saja yang ada didalam kampus ini. Untuk mengenalkan informasi tersebut kepada mahasiswa baru dapat dilakukan melalui berbagai media yang sudah umum digunakan. Namun untuk menambah pengalaman baru dalam mengenalkan lingkungan kampus tersebut diperlukan teknologi yang dapat memvisualisasikan tempat ruangan tersebut. Oleh karena itu diperlukan suatu media alternatif baru agar dapat memberikan gambaran visualisasi tersebut. Teknologi yang akan diterapkan adalah video 360 derajat dan virtual reality. Dengan memanfaatkan kelebihan dari teknologi ini maka pengguna tidak harus repot bergerak atau pergi ke tempat aslinya. Dengan mengubah keseluruhan bangunan kampus dengan perekaman video 360 derajat menjadi objek virtual, mahasiswa baru dapat mengetahui gambaran ruangan apa saja yang terdapat pada kampus dengan menjalankan aplikasi virtual reality menggunakan perangkat pendukung yaitu kacamata VR. Penulis menggunakan metode Multimedia Development Life Cycle (MDLC) dalam penelitian untuk melakukan perancangan sistem.

Combination of Support Vector Machine and Lexicon-Based Algorithm in Twitter Sentiment Analysis Rindu Hafil Muhammadi; Tri Ginanjar Laksana; Amalia Beladinna Arifa
Khazanah Informatika Vol. 8 No. 1 April 2022
Publisher : Department of Informatics, Universitas Muhammadiyah Surakarta, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/khif.v8i1.15213

Data from the Ministry of Civil Works and Public Housing (Kementrian PUPR) in 2019 shows that around 81 million millennials do not own houses. Government Regulation Number 25 of 2020 on the Implementation of Public Housing Savings, commonly called PP 25 Tapera 2020, is one of the government's efforts to ensure that Indonesian people can afford houses. Tapera is a deposit of workers for house financing, which is refundable after the term expires. Immediately after enaction, there were many public responses regarding the ordinance. We investigate public sentiments commenting on the regulation and use Support Vector Machine (SVM) in the study since it has a good level of accuracy. It also requires labels and training data. To speed up labeling, we use the lexicon-based method. The issue in the lexicon-based lies in the dictionary component as the most significant factor. Therefore, it is possible to update the dictionary automatically by combining lexicon-based and SVM. The SVM approach can contribute to lexicon-based, and lexicon-based can help label datasets on SVM to produce good accuracy. The research begins with collecting data from Twitter, preprocessing raw and unstructured data into ready-to-use data, labeling the data with lexicon-based, weighting with TF-IDF, processing using SVM, and evaluating algorithm performance model with a confusion matrix. The results showed that the combination of lexicon-based and SVM worked well. Lexicon-based managed to label 519 tweet data. SVM managed to get an accuracy value of 81.73% with the RBF kernel function. Another test with a Sigmoid kernel attains the highest precision at 78.68%. The RBF kernel has the highest recall result with a value of 81.73%. Then, the F1-score for both the RBF kernel and Sigmoid is 79.60%.

Sistem Rekomendasi Pemilihan Peminatan Menggunakan Density Canopy K-Means Ridho Ananda; Muhammad Zidny Naf’an; Amalia Beladinna Arifa; Auliya Burhanuddin
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 4 No 1 (2020): Februari 2020
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

The carelessly selection of specialization course leaves some students with difficulty. Therefore, it is needed a recommendation system to solve it. Several approaches could be used to build the system, one of them was K-Means. K-Means required the number of initial centroid at random, so its result was not yet optimal. To determine the optimal initial centroid, Density Canopy (DC) algorithms had been proposed. In this research, DC and K-Means (DCKM) was implemented to build the recommendation system in the problem. The alpha criterion was also proposed to improve the performance of DCKM. The academic quality dataset in the 2018 informatics programs students of ITTP was used. There were three main stages in the system, namely determination of the weight of the course in dataset, implementation of DCKM, and determination of specialization recommendations. The results showed that the system by using DCKM has good quality based on the Silhouette results (at least 0.655). The system also used standar valuation scale in ITTP and silhouette index in the process of system. The results showed that 176 (65.91%) students were recommended in IT specialization, 25 (9.36%) students were recommended in MM specialization and 66 (24.7%) students were recommended in SC specialization.

Penerapan Long Short Term Memory untuk Memprediksi Flight Delay pada Penerbangan Komersial Muhammad Genta Ari Shandi; Rifki Adhitama; Amalia Beladinna Arifa
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 4 No 3 (2020): Juni 2020
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Delay in airline services, become an unpleasant experience for passengers who experience it. This study aims to build a model that can predict flight delay (departure) using the Long Short Term Memory method and can find out its performance. In this study there are two scenarios that have different ways of preprocessing. Both of these scenarios produce predictions with error values calculated using Root Mean Squared Error (RMSE), respectively from the first to the second scenario namely: 41, 21. Between the two, the second scenario is better than the first scenario due to extreme data deletion ( anomaly) in the second scenario with an error value using RMSE of 0.116.

PERANCANGAN WEBSITE PENYEWAAN ALAT OUTDOOR MENGGUNAKAN FRAMEWORK LARAVEL PADA TOKO AKATARA OUTDOOR Theo Felix Harianto Purba; Novian Adi Prasetyo; Amalia Beladinna Arifa
KLIK- KUMPULAN JURNAL ILMU KOMPUTER Vol 9, No 2 (2022)
Publisher : Lambung Mangkurat University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20527/klik.v9i2.459

Technology is currently advancing at a breakneck pace, and it has already infiltrated the most important elements of human life. The internet can make it easier for humans to find and disseminate information, as well as websites that are rapidly growing in popularity, particularly in the business sector, with many businesspeople creating websites with the goal of selling, renting, and providing information about their company to obtain information. high ability to sell Akatara Outdoor Store is a store that provides outdoor equipment rental services. This website informs clients about the goods available for rent, and it is supposed to make it easier for store managers to promote their goods as well as make it easier for customers to rent them.Keywords: Black-box, Laravel, Rent, Waterfall, WebsiteTeknologi saat ini sangat berkembang pesat, dengan perkembangan teknologi yang pesat ini sudah menduduki aspek tertinggi dalam kehidupan manusia. Internet dapat mempermudah manusia untuk mencari informasi serta menyebarkan informasi hanya dengan melalui internet, sama halnya dengan website yang juga berkembang pesat khususnya dibidang bisnis dan banyak sekali pebisnis yang membuat website dengan tujuan barang yang dapat dijual, disewakan dan juga memberikan informasi tentang perusahaan mereka untuk mendapat daya jual yang tinggi. Toko Akatara Outdoor merupakan sebuah toko yang bergerak dibidang jasa penyewaan barang alat outdoor untuk. Website ini memberikan sebuah informasi kepada pelanggan tentang barang yang akan disewakan dan diharapkan dapat memudahkan pengelola toko untuk mempromosikan barangnya dan juga dapat memudahkan masyarakat dalam mencari informasi barang yang tersedia. Perancangan aplikasi menerapkan dengan metode waterfall dengan beberapa tahap seperti analisis kebutuhan, desain, pengkodean, pengujian, dan juga pemeliharaan. Kata kunci: Black-box, Laravel, Penyewaan, Waterfall, Website

Perbandingan Performa Antara Algoritma Naive Bayes Dan K-Nearest Neighbour Pada Klasifikasi Kanker Payudara Annisa Nugraheni; Rima Dias Ramadhani; Amalia Beladinna Arifa; Agi Prasetiadi
Indonesian Journal of Data Science, IoT, Machine Learning and Informatics Vol 2 No 1 (2022): February
Publisher : Research Group of Data Engineering, Faculty of Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/dinda.v2i1.391

Breast cancer is the second most common cause of death from cancer after lung cancer is in the first place. Breast cancer occurs when cells in breast tissue begin to grow uncontrollably and can disrupt existing healthy tissue. Therefore, there is a need for a classification to distinguish breast cancer patients and healthy people. Based on previous research, the Naïve Bayes and K-Nearest Neighbor algorithms are considered capable of classifying breast cancer. In the research process using the breast cancer dataset from the Breast Cancer Coimbra dataset in 2018 UCI Machine Learning Repository with a total of 116 data, while for the calculation of the feasibility of the method using the Confusion Matrix (Accuracy, Precision, and Recall) and the ROC-AUC curve. The purpose of this study is to compare the performance of the Naïve Bayes and K-Nearest Neighbor algorithms. In testing using the Naïve Bayes algorithm and the K-Nearest Neighbor algorithm, there are several test scenarios, namely, data testing before and after normalization, model testing based on a comparison of training data and testing data, model testing based on K values in K-Nearest Neighbors, and model testing. based on the selection of the strongest attribute with the Pearson correlation test. The results of this study indicate that the Naïve Bayes algorithm has the highest average accuracy of 69.12%, healthy precision 64.90%, pain precision 83%, healthy recall 88%, sick recall 61.11% and AUC 0.82 which is included in the good classification category. Meanwhile, the highest average results of the K-Nearest Neighbor algorithm are 76.83% for accuracy, 76% healthy precision, 80.21% pain precision, 74.18% for healthy recall, 80.81% sick recall and 0.91 AUC which is included in the excellent classification category.

Prediksi Penyakit Ginjal Kronis Menggunakan Hibrid Jaringan Saraf Tiruan Backpropagation dengan Particle Swarm Optimization Sheren Afryan Tiastama; Tri Ginanjar Laksana; Amalia Beladinna Arifa
Journal of Innovation Information Technology and Application (JINITA) Vol 3 No 1 (2021): JINITA, June 2021
Publisher : Politeknik Negeri Cilacap

The number of Chronic Kidney Disease patient increased year by year while it doesn’t following by sufficient human resources and infrastructure needs the information of Chronic Kidney Disease patient prediction. Prediction of Chronic Kidney Disease patient is necessary to be done as an anticipation for preparing the better human resources and infrastructure that will effect to patient survival rate. In this study, backpropagation artificial neural network and particle swarm optimization combination used to predict the number of Chronic Kidney Disease patient. Artificial Neural Network has the ability in time series data prediction, such as the number of Chronic Kidney Disease year by year. But, backpropagation artificial neural network has a weakness in weight inisialization which taken unoptimally that could cause bad convergence speed. Particle swarm optimization will resolve the backpropagation artificial neural network weakness by weights optimization that will used in backpropagation artificial neural network. The Artificial Neural Network and Particle Swarm Optimization have several parameters, such as the number of hidden layer neuron, learning rate, and swarm. This research is using RSUD Banyumas Chronic Kidney Disease patient data in 2011 until 2020. Matlab R2019a used in this research as a software to predict chronic kidney disease patient data. The test results shows the prediction accuracy based on Mean Squared Error value is 0,0370 using 12-16-1 artificial neural network architecture, 0.005 learning rate, 1250 epochs and 50 swarms

SISTEM PAKAR DIAGNOSIS HAMA DAN PENYAKIT PADA TANAMAN KANGKUNG MENGGUNAKAN METODE FORWARD CHAINING Iffan Ahmad Nasrulloh; Pradana Ananda Raharja; Amalia Beladinna Arifa
Jurnal SAINTEKOM Vol. 12 No. 2 (2022): September 2022
Publisher : STMIK Palangka Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33020/saintekom.v12i2.321

The production of water spinach vegetable crops in 2020 in Central Java ranks 2nd in water spinach production, which is 29,108 tons. The amount of water in spinach production and the increased risk of being attacked by pests and diseases will be even more significant, and proper care is needed to improve the quality of water spinach. It takes an expert to convey knowledge about controlling pests and diseases that are good and right. From these problems, we developed a system to overcome the problem of water spinach plant disease. This expert system uses the forward chaining method. The results of this study are a pest and disease diagnosis system on water spinach using a website-based forward chaining method with accuracy testing using the confusion matrix method by testing 50 rules. Of 50 data, 45 are considered appropriate, and the results of 5 rules are not relevant, so the evaluation results of this expert system produce system accuracy with a percentage of 90%.

Co-Authors Agi Prasetiadi Ananda Rifkiy Hasan Annisa Nugraheni Auliya Burhanuddin Gita Fadila Fitriana Hapsari Warih Utami Herdiesel Santoso Iffan Ahmad Nasrulloh laksana, tri ginanjar Muhammad Genta Ari Shandi Muhammad Zidny Naf’an Novian Adi Prasetyo Raharja, Pradana Ananda Ridho Ananda Rifki Adhitama Rima Dias Ramadhani Rindu Hafil Muhammadi Sheren Afryan Tiastama Theo Felix Harianto Purba Vico Meylana Eka Putra

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