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All Journal GEMA TEKNOLOGI Techno.Com: Jurnal Teknologi Informasi Jurnal Simetris Syntax Jurnal Informatika Elkom: Jurnal Elektronika dan Komputer Prosiding SNATIF Jurnal Teknologi Informasi dan Ilmu Komputer Seminar Nasional Informatika (SEMNASIF) CESS (Journal of Computer Engineering, System and Science) E-Dimas: Jurnal Pengabdian kepada Masyarakat Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) SISFOTENIKA Journal of Information Technology and Computer Science (JOINTECS) JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Jurnal Ilmiah FIFO Jurnal Pilar Nusa Mandiri InComTech: Jurnal Telekomunikasi dan Komputer Prosiding Seminar Nasional Teknoka JRST (Jurnal Riset Sains dan Teknologi) SINTECH (Science and Information Technology) Journal JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Jiko (Jurnal Informatika dan komputer) Explore IT : Jurnal Keilmuan dan Aplikasi Teknik Informatika Jurnal Telematika STRING (Satuan Tulisan Riset dan Inovasi Teknologi) Jurnal Ilmiah Ilmu Komputer Fakultas Ilmu Komputer Universitas Al Asyariah Mandar Jurnal Ilmu Komputer dan Bisnis Techno Xplore : Jurnal Ilmu Komputer dan Teknologi Informasi Jurnal Asiimetrik: Jurnal Ilmiah Rekayasa Dan Inovasi SKANIKA: Sistem Komputer dan Teknik Informatika Jurnal Pewarta Indonesia Ascarya: Journal of Islamic Science, Culture and Social Studies Multica Science and Technology Jurnal PkM (Pengabdian kepada Masyarakat) Bit (Fakultas Teknologi Informasi Universitas Budi Luhur) Prosiding Seminar Nasional Sisfotek (Sistem Informasi dan Teknologi Informasi) Indonesian Journal of Computer Science Jurnal Ticom: Technology of Information and Communication Journal of Systems Engineering and Information Technology Jurnal Teknik Indonesia
Arief Wibowo
Universitas Budi Luhur
Religion Arts Humanities Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Physics Social Sciences Transportation

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Perbandingan Kinerja Algoritma Support Vector Machine dan K-Nearest Neighbor Terhadap Analisis Sentimen Kebijakan New Normal Didin Muhidin; Arief Wibowo
STRING (Satuan Tulisan Riset dan Inovasi Teknologi) Vol 5, No 2 (2020)
Publisher : Universitas Indraprasta PGRI Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (428.443 KB) | DOI: 10.30998/string.v5i2.6715

Abstract

Twitter is one of the popular microblogging sites among internet users, so that many people use Twitter to convey their positive and negative sentiments towards the new normal policy. The pandemic period raises much public sentiment towards the policy of adapting to the new normal. This study aims to classify sentiment tweets into positive and negative classes. The classification algorithms used are k-NN and SVM. The test results show that the k-NN algorithm is better than SVM in solving this sentiment case with an accuracy of 72.96%.
Komparasi Pengelompokan Pemeringkatan Sertifikasi Travel Umrah Berizin dengan Algoritma Klasterisasi K-Means dan K-Medoids Muhammad Risky; Arief Wibowo; Zakaria Anshori
InComTech : Jurnal Telekomunikasi dan Komputer Vol 12, No 1 (2022)
Publisher : Department of Electrical Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/incomtech.v12i1.14528

Abstract

Dengan Terbitnya Undang-Undang Nomor 11 Tahun 2020 tentang Cipta Kerja yang merevisi beberapa pasal dalam Undang-Undang Nomor 8 Tahun 2019 tentang Penyelenggaraan Haji dan Umrah, Kementerian Agama harus melakukan pembahasan tentang peraturan turunankedua Undang-Undang tersebut. Di antara peraturan turunan yang diterbitkan adalah Keputusan Menteri Agama (KMA) Nomor 1251 Tahun 2021 tentang Skema dan Kriteria Akreditasi serta Sertifikasi Usaha Penyelenggaraan Ibadah Umrah dan Penyelenggaraan Haji Khusus. Dalam KMA ini, Kementerian Agama melaksanakan pengaturan berkenaan dengan pemeringkatan PPIU dan juga PIHK, yang dibagi pemeringkatan menjadi 3 kelompok yaitu A, B C. Penelitian ini bertujuan untuk menganalisa dengan pembanding atau referensi lain menggunakan metode penambangan (mining). Penambangan (mining) yang dipergunakan pada penelitian ini adalah terhadap data. Dataset akan di proses dengan algoritma yang ditemukan oleh Lloyd dan kawan-kawan, yakni K-Means. Selain itu, dataset juga akan diproses dengan salah satu algoritma lain untuk pengelompokan data, dalam hal ini peneliti memilih K-Medoids. Dataset terdiri dari 5.000 baris data sesuasi dengan penilaian indikator dominan dan ko-dominan. Hasil Penelitian menunjukkan bahwa metode K-Means dengan dua kelompok dengan maksimize tanpa normalize memiliki Davies-Bouldin Index (DBI) 0,234. Sedangkan metode K-Means dengan 2 kelompok serta melakukan normalize maka Davies-Bouldin Index (DBI) adalah 0,005. K-Means adalah yang paling optimal dibanding K-Medoids pada penelitian ini.
Penentuan Dosen Terbaik Menggunakan Metode Analytical Hierarchy Process (AHP) dan Technique For Order By Similarity To Ideal Solution (TOPSIS): Studi Kasus Akademi Teknologi Bogor Istiqoomatun Nisaa; Arief Wibowo

Publisher : Program Studi Teknik Informatika, Fakultas Teknik, Universitas Yudharta Pasuruan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35891/explorit.v12i2.2288

Abstract

Akademi Teknologi Bogor yang berdiri sejak tahun1997, berlokasi di Kota Bogor. Didukung 40 staf dosen. Dosen mempunyai kedudukan sebagai tenaga professional pada jenjang pendidikan tinggi yang diangkat sesuai dengan peraturan perundang-undangan. Dosen adalah tenaga pendidik yang memberikan sejumlah ilmu pengetahuan kepada anak didik di Perguruan Tinggi. Sistem penentuan dosen terbaik digunakan untuk mendukung kegiatan belajar dan mengajar dikampus agar terciptanya mahasiswa yang berkualitas dan kompeten di bidangnya. Hal ini untuk memenuhi kriteria dosen untuk memutuskan dosen yang dianggap terbaik. Proses penentuan dosen terbaik pada sistem yang berjalan saat ini masih memiliki kekurangan yaitu membutuhkan waktu yang lama untuk memproses data hasil kuesioner, sehingga keputusan yang didapat belum valid. Pada penelitian ini akan dibuat sebuah Sistem Pendukung Keputusan (SPK) dimana sistem ini dapat membantu seseorang mengambil keputusan yang akurat dan tepat sasaran. Adapun metode yang digunakan yaitu metode Analytical Hierarchy Process (AHP) untuk menghitung bobot setiap kriteria dan Technique For Order By Similarity To Ideal Solution (TOPSIS) untuk merangking alternatif berdasarkan setiap kriteria. Hasil penelitian ini adalah sebuah sistem yang mampu menghasilkan urutan perangkingan dalam penentuan dosen terbaik di Akademi Teknologi Bogor.
SEGMENTASI PELANGGAN MENGGUNAKAN METODE K-MEANS CLUSTERING BERDASARKAN MODEL QRF PADA PERUSAHAAN RINTISAN PENYEDIA TENAGA KERJA Sari Anggar Kusuma Melati; Arief Wibowo
JURNAL ILMU KOMPUTER Vol 6 No 2 (2020): Edisi September
Publisher : LPPM Universitas Al Asyariah Mandar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35329/jiik.v6i2.138

Abstract

The difficulty of getting a job that is in accordance with the interests and specialization of a worker, as well as the difficulty of the company getting a worker who suits the needs of the company causes the mushrooming of consulting firms or labor providers in Indonesia today. With the increasing number of companies providing labor, of course the competitiveness of the business industry in the human resources is increasingly high. So it needs to be analyzed to determine the right business strategy, such as determining the company's promotion goals. One of them is analyzing the segmentation of customers who have worked together. This research successfully modeled customer segmentation based on data mining clustering techniques using the K-Means data mining algorithm. The QRF (Quantity, Recency, Frequency) modeling process is analyzing the customer's behavior from the number of requests in each transaction carried out within a certain timeframe, as well as recency as the identification of the time span of the last transaction, as well as the number of transactions made within a certain time period. Researchers conducted a period of data for one year by analyzing customer activity in start-up providers of labor during 2019, on 86 active customers. Based on the analysis results obtained, customer segmentation in two clusters with QRF (Quantity, Recency, Frequency) modeling using Davies Bouldin Index (DBI) evaluation scored -0,482, while customer segmentation in three clusters using QRF (Quantity, Recency, Frequency) evaluation using Davies Bouldin Index (DBI) evaluation to obtain -0.469, and customer segmentation in four clusters with QRF (Quantity, Recency, Frequency) modeling using Davies BouldinIndex (DBI) evaluation to obtain -0,526. Keywords— pelanggan, clustering, algoritma k-means, DBI, QRF
Penerapan Data Mining pada Suku Bunga Investasi Deposito di Indonesia Menggunakan Metode K-Means Clustering untuk Pengelompokan Profitabilitas Raden Sasongko; Arief Wibowo
Ascarya: Journal of Islamic Science, Culture, and Social Studies Vol. 2 No. 1 (2022)
Publisher : Perkumpulan Alumni dan Santri Mahyajatul Qurro'

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Investment in Indonesia has several types, one of which is deposits through banks, namely individual or collective customers lending excess funds to banks and channeling them in the form of credit. This research is about clustering on several Banks, those are Regional, Private, Foreign, Persero and General Banks which have different interest rates for investments. The method used in this study is the Knowledge Discovery using Database (KDD) method, using RapidMiner tools. The algorithm used to perform clustering is the K-Means algorithm. The data that was used is data on investment credit interest rates from several banks in Indonesia obtained from BPS (Central Statistics Agency) Indonesia. This data is taken from 2009-2020. The clustering results obtained are 3 clusters, where cluster 1 is Regional Bank, which turns out to have stable and high loan interest rates, then cluster 2 is Foreign Bank, which also has the lowest and unstable interest rate. While cluster 3 is a private bank and a state-owned company, these two banks have similar interest rates and levels of stability. In addition, these two banks are in the middle for investment options.
SURVEI METODE PENGUKURAN APLIKASI CHATBOT BERBASIS MEDIA SOSIAL Ratna Ayu Sekarwati; Ahmad Sururi; Rakhmat Rakhmat; Miftahul Arifin; Arief Wibowo
Gema Teknologi Vol 21, No 2 (2021): October 2020 - April 2021
Publisher : Vocational School Diponegoro University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/gt.v21i2.36170

Abstract

The design of Chatbot aims to facilitate social activities in all areas to be considered. Chatbot is one type of machine that can communicate with humans using natural language. Communication happening via chat is a written conversation. Chatbot is a form of application implementation from Natural Language Processing (NLP) that belongs to one branch of artificial intelligence or Artificial Intelligent (AI). Social Media now provides a service that allows developers to process and integrate chatbot applications. This paper aims to review the papers that build chatbot applications in various social media using various testing methods. The contribution of this paper is to determine which method is able to measure the level of chatbot accuracy well. This review paper will choose the equation of the most widely used test methods and social media from various papers so that further research is expected to implement the right testing methods and use better social media in terms of user experience, features, and services. The review paper shows that the Black-box and System Usability Scale testing methods are most used in the review paper. This testing method is a type of method that performs testing of the flow and how the chatbot works to achieve functional validation throughly.
Algoritme K-Means dalam Pengelompokan Kantor Cabang untuk Optimalisasi Manajemen Perbankan Angga Ardhianto; Bowo Relawanto; Arief Wibowo
Jurnal Telematika Vol 15, No 2 (2020)
Publisher : Institut Teknologi Harapan Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Branch segmentation is needed by companies to facilitate management in planning and managing these branches. Mistakes in determining the segmentation or grouping of branches will have an impact on planning or management, such as the efficiency of operational costs, calculation of branch performance, operational supervision, and optimization of company resources. As a first step in optimizing branch management, optimal branch grouping, or according to branch similarities, can be adjusted to the size or size of a branch. In the supervisory and monitoring functions, this grouping is also a consideration and prioritization of supervision, where large branches will of course use different plans with smaller branches. In setting the budget to avoid potential fraud, it is best if the operating budget is adjusted according to the size of the branch. This study uses the K-Means algorithm to classify branch offices based on transactions per month and the number of types of transactions according to the required segmentation. Branches can be grouped into large, medium, and small groups. The results show that the K-Means algorithm can produce bank branch groupings based on the number of types of transactions and the average transaction per month, which is divided into three clusters. The three clusters are the large branch cluster or cluster 1, the intermediate branch cluster or cluster 2, and the small branch cluster or cluster 3. The test uses the Davies Bouldin Index of 0.5.Segmentasi cabang diperlukan perusahaan untuk mempermudah manajemen dalam membuat perencanaan dan pengelolaan cabang-cabang tersebut. Kesalahan penentuan segmentasi atau pengelompokan cabang akan berdampak pada perencanaan atau pengelolaan, seperti pada efisiensi biaya operasional, penghitungan kinerja cabang, pengawasan operasional, dan optimalisasi sumber daya perusahaan. Sebagai langkah awal dalam optimalisasi pengelolaan cabang, pengelompokan cabang yang optimal, atau sesuai dengan kemiripan cabang, bisa disesuaikan dengan besar atau kecilnya sebuah cabang. Dalam fungsi pengawasan dan pemanatauan, pengelompokan ini juga menjadi pertimbangan dan prioritisasi pengawasan, di mana cabang besar tentunya akan menggunakan perencanaan yang berbeda dengan cabang yang lebih kecil. Dalam pengaturan anggaran untuk menghindari potensi fraud, sebaiknya anggaran operasional disesuaikan dengan besarnya cabang tersebut. Penelitian ini menggunakan algoritme K-Means untuk mengelompokkan kantor cabang berdasarkan transaksi per bulan dan jumlah jenis transaksi sesuai dengan segmentasi yang dibutuhkan. Cabang dapat dikelompokkan menjadi kelompok besar, sedang, dan kecil. Hasil penelitian menunjukan bahwa algoritme K-Means ini dapat menghasilkan pengelompokan cabang-cabang bank berdasarkan jumlah jenis transaksi dan rata-rata transaksi per bulan yang dibagi menjadi tiga cluster. Ketiga cluster itu adalah clustercabang besar atau cluster 1, clustercabang menengah atau cluster 2, dan clustercabang kecil atau cluster 3. Pengujian menggunakan Davies Bouldin Index sebesar 0,5.
Prediction of Feasibility of Entrepreneurial Proposals in Student Creativity Program Harun Nasrullah; Endah Sarah Wanty; Arief Wibowo
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol 6, No 1 (2022): Issues July 2022
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v6i1.7253

Abstract

Student Creativity Program is a program organized by the Directorate of Learning and Student Affairs, Directorate General of Higher Education, Research, and Technology, Ministry of Education, Culture, Research and Technology as a national level student creativity event as an effort to grow, accommodate, and realize students' creative and innovative ideas. Based on 2017-2021 data, each year an average of 63,337 proposals are received, administrative and substance evaluations involve complex assessment components and are carried out manually so that it takes a relatively long time in the calculation process. Then a special method is needed that speeds up the processing of assessment data. This research was conducted on the substance of the Entrepreneurship Sector to predict the feasibility of a proposal to get funding applying data mining with the Naive Bayes Classifier (NBC) and K-Nearest Neighbor (K-NN) algorithms with a comparison between Euclidean Distance and Manhattan Distance. From the results, it is known that NBC produces 96.49% accuracy and 0.912 Kappa. K-NN with the largest Euclidean Distance calculation in K-5, K-7 and K9 with an accuracy of 99.04% and Kappa 0.975, K-NN Manhattan Distance calculation produces the greatest accuracy of all the methods used by researchers, namely 100% and Kappa 1,00 categorized as Excellent. So the conclution is that the K-NN method with K-5 which produces the greatest accuracy and Kappa can be recommended to PKM stakeholders in funding feasibility algorithms.
Analisis Sentimen Opini Masyarakat Terhadap Keefektifan Pembelajaran Daring Selama Pandemi COVID-19 Menggunakan Naïve Bayes Classifier Ari Wibowo; Firman Noor Hasan; Luthfi Akbar Ramadhan; Rika Nurhayati; Arief Wibowo
Jurnal Asiimetrik: Jurnal Ilmiah Rekayasa Dan Inovasi Volume 4 Nomor 2 Tahun 2022
Publisher : Fakultas Teknik Universitas Pancasila

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35814/asiimetrik.v4i1.3577

Abstract

Since Indonesia was affected by the Covid-19 pandemic, one of the sectors affected was Education. The government makes an online learning system policy where the system is run with an online process. Not a few of them complained about the limitations of activities issued by the government. Twitter social media is often used to express opinions about concerns about programs issued by the government. The Twitter data crawling process was carried out using the hashtag "learning from home" to get as many as 1,000 datasets, followed by the process of removing duplicates which left 524 datasets and then carrying out the implementation stage of the Naïve Bayes Classifier Algorithm. The purpose of this study was to determine the number of positive and negative sentiments from the dataset labeling classification and to determine the accuracy results of using the Naïve Bayes Classifier method as well as the results of evaluation tests on positive and negative sentiment datasets. Based on the experiment, positive sentiment was obtained as many as 480 and negative sentiment as many as 44 out of 524 datasets. The accuracy results in the evaluation test process get results of 88.5% where negative sentiments get a precision value of 12%, recall 17%, and f1-score 14%, while positive sentiments get a precesion value of 95%, recall 93%, and f1 -score 94%.
Data Mining Klasterisasi dengan Algoritme K-Means untuk Pengelompokkan Provinsi Berdasarkan Konsumsi Bahan Bakar Minyak Nasional Arief Wibowo; Indah Rizky Mahartika
Prosiding SISFOTEK Vol 3 No 1 (2019): SISFOTEK 2019
Publisher : Ikatan Ahli Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (517.822 KB)

Abstract

Petroleum is one of the natural resources that play an important role in human life, mainly used as the fuel needed by all levels of society. The distribution of fuel oil (BBM) in Indonesia is carried out by the Downstream Oil and Gas Regulatory Agency (BPH Migas). With the availability of data on fuel consumption in each province, it can be seen that the pattern of fuel consumption in Indonesia is beneficial for regulators in the management of fuel distribution. To find out the pattern of national fuel consumption, we need a model of grouping regions in Indonesia based on the level of fuel consumption in each province. This study analyzes data on national fuel consumption throughout Indonesia using the Data Mining Clustering technique, and the Euclidean Distance measurement method. The final results of this study indicate that the K-Means algorithm can group provinces based on national fuel consumption levels into three clusters with their respective specifications. Modeling results were evaluated using the Davies Bouldin Index (DBI) instrument, with a value of 0.32. The results of testing using DBI approaching 0 indicate that the clusters formed are relatively very good and ideal.
Co-Authors - Arientawati Abdul Ghapur Afifah Khaerani Agustia Hananto Ahmad Sururi Ahmad Sururi Alif Dewan Daru Wibiyanto Andy Rio Handoko Angga Ardhianto Anindya Putri Pradiptha Anita Diana Anugrah Sandy Yudhasti Apriati Suryani Ari Wibowo Arief Umarjati Asep Permana Bayu Sadewo Bowo Relawanto Danar Wido Seno Dedy Mirwansyah Didin Muhidin Diva Ajeng Brillian Risaychi Dwi Yulianti Dyah Retno Utari Dyah Retno Utari, Dyah Retno Endah Sarah Wanty Farah Chikita Venna Firman Noor Hasan Fransiska Vina Sari Frenda Farahdinna Fried Sinlae Hadidtyo Wisnu Wardani Harun Nasrullah Henry Indah Rizky Mahartika Inge Virdyna Irfan Nurdiansyah Istiqoomatun Nisaa Jumaryadi, Yuwan KRESNO YULIANTO Kurnia Setiawan Larasati, Pamela Lingga Desyanita Luthfi Akbar Ramadhan Maria Adiningsih Megananda Hervita Permata Sari Miftahul Arifin Miftahul Arifin Mochammad Rizky Royani Moh Makruf Muhammad Risky Mulyati Mulyati Nendi Nendi Pattipeilohy, William Frado Pattipeilohy, William Frado Raden Sasongko Rakhmat Rakhmat Rakhmat Rakhmat Ratna Ayu Sekarwati Ratna Ayu Sekarwati Rika Nurhayati Riki Ramdani Saputra Rina Megawati Ruliana, Poppy Saptari Wijaya Mulia Sari Anggar Kusuma Melati Sari, Fransiska Vina Satiri, Satiri Selly Rahmawati Septian Firman S Sodiq Septiani, Riska Shofinurdin Shofinurdin Sigit Budi Nugroho Sitti Aliyah Azzahra Sujiharno Sujiharno Vasthu Imaniar Ivanoti Wahyu Cesar Wahyu Desena Wahyudi, Widi Widiyaningrum, Diyah Kiki Yofita Sandra, S.Pd., M.Pd., Zico Farlin, Dr. Budiwirman, M.Pd., Yogi Ajeng Ningrum Zakaria Anshori