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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Jurnal Teknologi Informasi dan Ilmu Komputer Sistemasi: Jurnal Sistem Informasi JOIV : International Journal on Informatics Visualization Sinkron : Jurnal dan Penelitian Teknik Informatika SMARTICS Journal INTECOMS: Journal of Information Technology and Computer Science J-SAKTI (Jurnal Sains Komputer dan Informatika) Jusikom: Jurnal Sistem Informasi Ilmu Komputer Zonasi: Jurnal Sistem Informasi Buana Information Technology and Computer Sciences (BIT and CS) REMIK : Riset dan E-Jurnal Manajemen Informatika Komputer JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) JIKA (Jurnal Informatika) Jurnal Sistem Komputer dan Informatika (JSON) Infotek : Jurnal Informatika dan Teknologi Journal of Applied Data Sciences Jurnal Cahaya Mandalika Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) Jurnal Pendidikan dan Teknologi Indonesia International Journal of Computer and Information System (IJCIS) Jurnal Informatika dan Teknologi Komputer ( J-ICOM) Djtechno: Jurnal Teknologi Informasi KLIK: Kajian Ilmiah Informatika dan Komputer J-SAKTI (Jurnal Sains Komputer dan Informatika) Jurnal Mandiri IT Journal of Informatics and Communication Technology (JICT) Malcom: Indonesian Journal of Machine Learning and Computer Science JUSIFOR : Jurnal Sistem Informasi dan Informatika Innovative: Journal Of Social Science Research Jurnal Sistem Informasi dan Manajemen VISA: Journal of Vision and Ideas INTERNAL (Information System Journal) Journal of Informatics and Communication Technology (JICT) IKRAM: Jurnal Ilmu Komputer Al Muslim
Huda, Baenil
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Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Public Health Social Sciences Other

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Determination of Training Participants in Community Work Training Centers Using the Naïve Bayes Classifier Algorithm Hananto, April Lia; Hananto, Agustia; Huda, Baenil; Rahman, Aviv Yuniar; Novalia, Elfina; Priyatna, Bayu
JOIV : International Journal on Informatics Visualization Vol 8, No 3 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.3.1995

Abstract

Community work training centers are skills training institutions that aim to improve the skills of the surrounding community by providing training programs that align with industry needs. Registration of training participants at the Al-Ikhwan Islamic Boarding School community work training centers often faces obstacles, namely, the selection process is still manual, so it takes a long time, and there is a possibility of errors. This study aims to apply the Naive Bayes Classifier Algorithm to determine whether applicants pass training at the Al-Ikhwan Islamic Boarding School community work training centers. This classification method is used to help optimize the applicant selection process by considering administrative factors, income, and training quotas. RapidMiner software is used as a tool to implement the algorithm. This study found that the Naive Bayes Classifier Algorithm can provide good accuracy results in determining applicants who pass the training selection. The test results show that the resulting model has an accuracy of 90.00% in determining passing training participants with data that has the highest chance of passing, namely data that has the attributes of the female gender, age 20 years, last education Senior High School/Vocational High School, student work/student, income 364,912, father's work as laborer, father's income 3912,280, mother's work as an IRT, and mother's income 885,964. This research increases efficiency and accuracy in determining training applicants at the Al-Ikhwan Islamic Boarding School community work training centers.
RANCANG BANGUN SISTEM INFORMASI E-COMMERCE AYAMSEGAR.ID MENGGUNAKAN METODE PROTOTYPE PADA UMKM Atmaja, Rashelin Zahra; Hananto, Agustia; Huda, Baenil; Hananto, Aprilia
Jurnal Informatika Vol 9, No 4 (2025): JIKA (Jurnal Informatika)
Publisher : University of Muhammadiyah Tangerang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31000/jika.v9i4.14469

Abstract

Transformasi digital telah menjadi kebutuhan mendesak bagi UMKM di era industri 4.0, terutama dalam aspek pemasaran dan layanan pelanggan. Penelitian ini bertujuan untuk merancang sistem informasi e-commerce berbasis website pada UMKM Kurnia Farms dengan menggunakan metode prototype. Proses pengembangan dilakukan secara iteratif, dimulai dari identifikasi kebutuhan, perancangan sistem, implementasi prototype, hingga evaluasi oleh pengguna. Pengumpulan data dilakukan melalui observasi, wawancara, studi pustaka, dan pengujian usability menggunakan System Usability Scale (SUS). Hasil implementasi menunjukkan bahwa sistem berhasil memenuhi kebutuhan pengguna dalam pengelolaan produk dan transaksi secara digital. Evaluasi usability menghasilkan skor SUS sebesar 87.5 yang termasuk dalam kategori Excellent Usability, menandakan bahwa sistem mudah digunakan dan sesuai dengan ekspektasi pengguna. Sistem ini dinilai dapat menjadi solusi digitalisasi yang efektif dan terjangkau bagi UMKM serupa.
Sistem Pemilihan Supplier Obat Menerapkan Metode Additive Ratio Analysis (ARAS) Al Khadzik, Fahmi; Huda, Baenil; Novalia, Elfina; Hilabi, Shofa Shofiah
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 5 No. 3 (2024): Maret 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v5i3.7499

Abstract

Qita Sehat pharmacy provides a wide range of medicines that are supplied by more than 30 suppliers and 100 buyers every month, but not all suppliers can meet the criteria set by pharmacies and suppliers are often late in the process of supplying drugs to pharmacies so that the stock in pharmacies is running low. From these problems, a solution is made, namely a drug supplier selection system is made by determining the priority order of drug suppliers with several criteria that match the availability of drugs at Qita Sehat pharmacies. The method used is the method of ARAS (Additive Ratio Analysis). The criteria considered are price, quality, lead time, communication systems, performance history and repair services. The result of this method is the order of priority of drug suppliers and knowing the results of the questionnaire through the sensitivity test that is the influence of changes in the value of the importance of the criteria. From the data generated in research using the ARAS method, the results obtained are that PT Javas Karya is the best supplier with the first rank of alternative A6 with a total value of 0.120.
Klasterisasi Siswa Berdasarkan Profil Akademik dan Karakteristik Belajar Menggunakan Algoritma K-Means untuk Mendukung Pembelajaran Faiharani, Attaya; Huda, Baenil; Nurapriani, Fitria; Hananto, April Lia
Journal of Information System Research (JOSH) Vol 7 No 3 (2026): April 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v7i3.9572

Abstract

Grouping students based on academic and non-academic characteristics is important to support the development of more targeted educational guidance strategies in schools. The main problem addressed in this study is the absence of objective data-based student mapping, which causes development programs to remain general and less targeted. This study aims to classify students using the K-Means clustering algorithm based on academic profiles and other supporting variables, and to evaluate cluster quality using the silhouette coefficient method. The research stages include data preprocessing, determining the optimal number of clusters, clustering using K-Means, and evaluating the clustering result. The results showed that four clusters were selected as the final configuration with a silhouette score of 0,1093, with cluster membership distributed into 12, 4, 2, and 2 students. Visualization using principal component analysis shows that most clusters are sufficiently well separeted. This study contributes a data-driven student grouping model that can be used as a basis for recommending student potential development according to the characteristics of each group.
KLASIFIKASI DAERAH RAWAN PENYAKIT HEWAN MENULAR STRATEGIS MENGGUNAKAN ALGORITMA DECISION TREE C4.5 Hasibuan, Nadya Susanti; Huda, Baenil; Hilabi, Shofa Shofiah; Hananto, April Lia
Djtechno: Jurnal Teknologi Informasi Vol 7, No 1 (2026): April
Publisher : Universitas Dharmawangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46576/djtechno.v7i1.8543

Abstract

Penelitian ini memiliki tujuan untuk mengklasifikasikan tingkat kerawanan daerah berdasarkan kasus penyakit hewan menular di wilayah kabupaten atau kota Provinsi Nusa Tenggara Barat dengan memanfaatkan teknik data mining. Dalam penelitian ini, metode yang diterapkan adalah algoritma Decision Tree C4.5 dengan menggunakan teknik evaluasi 5-fold cross validation sebagai penilaian efektivitas model. Dataset yang digunakan merupakan data sekunder periode 2020–2024 yang melalui tahapan preprocessing meliputi data cleaning, agregasi, feature engineering, dan pelabelan menjadi tiga kategori kerawanan: rendah, sedang, dan tinggi. Temuan dari penelitian ini menunjukkan bahwa variabel rata-rata kasus menjadi faktor utama dalam menentukan tingkat wilayah kerawanan. Model yang dibangun mampu menghasilkan klasifikasi dengan performa yang baik, ditunjukkan oleh akurasi rata-rata sebesar 94% dan nilai tinggi untuk presisi, recall, serta F1-score di setiap kategori. Selain itu, model memiliki kemampuan interpretasi yang jelas melalui struktur pohon keputusan yang dihasilkan. Dengan demikian, pendekatan yang digunakan terbukti efektif dalam identifikasi wilayah berisiko dan dapat mendukung pengambilan keputusan dalam upaya pengendalian penyakit hewan menular dengan strategi yang lebih tepat dan berbasis data.
Prioritizing micro, small, and medium enterprises assistance areas in West Java using analytical hierarchy process Lestari, Renita; Huda, Baenil; Novalia, Elfina; Hananto, April Lia
Jurnal Mandiri IT Vol. 14 No. 4 (2026): April: Computer Science and Field.
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mandiri.v14i4.527

Abstract

This study aims to develop a Decision Support System (DSS) to prioritize areas for receiving assistance for Micro, Small, and Medium Enterprises (MSMEs) in West Java Province using the Analytical Hierarchy Process (AHP) and Simple Additive Weighting (SAW) methods. The AHP method is used to determine the importance weight of each criterion based on its priority level, while the SAW method is used to carry out the normalization process, calculate preference values, and rank alternative areas. The criteria used include the number of MSMEs, workforce, financial stability ratio, legality ratio, BPP ratio, digital ratio, and innovation ratio. The results of the study indicate that the system built is able to produce an objective and consistent ranking of priority areas for MSME assistance, as evidenced by the agreement between the results of manual calculations using Microsoft Excel and the results of calculations in the system. Thus, this system is expected to assist relevant parties in making decisions regarding the distribution of MSME assistance in a more targeted and structured manner and rank 27 administrative regions in West Java Province. The results show that the highest-ranked region achieved a preference value of 0.8573, indicating its highest priority for MSME assistance, while the lowest-ranked region obtained a value of 0.5129. These results demonstrate the system’s capability to generate consistent and objective rankings. In addition, this study contributes by applying a combined AHP–SAW approach at a regional (macro) level, which is still limited in previous studies, thereby providing a more comprehensive framework for data-driven policy decision-making.
Co-Authors Agustina, Alvi Akbari, Wahyu Azriel Al Khadzik, Fahmi Alparizi, Muhamad Iqbal Apriani, Fitria April Lia Hananto Arkan Hilman Hakim Atmaja, Rashelin Zahra Aviv Yuniar Rahman baktria, leonyka Danny Manongga Eko Sediyono Faiharani, Attaya Fauzi Ahmad Muda Ferdiansyah, Indra Hananto, Agustia Hasibuan, Nadya Susanti Hasna Azhari, Soraya Hendry Hilabi, Shofa Hilabi, Shofa Shofia Hilabi, Shofa Shofiah Hilabi, Shofa Sofiah Hindriyanto Dwi Purnomo Ihsan, Mohammad Maftuh Ikhsan, Muhammad Daffa Irwan Sembiring Ismail, Dennisa Avriel Iwan Setiawan kastiawan, Nurhayadi Kasuma, Hidayatul Rahman Kurniawan, Aldi Lestari, Renita Muhamad Bayu Aditya Pratama Muslih, Muhamad Novalia, Elfina Nur ‘Azah Nurajizah, Dhea Nurapriani, Fitria Nurfauzi, Oka Muhamad Nursan Alfarizi, Maulana Paryono, Tukino Prayono, Tukino Primatrias, Riftan Priyatna, Bayu Purba, Arif Budimansyah Rismoko, Rismoko Rosalina, Elsa Saptiani, Anita Saputri, Arini Setiawan, Revi Shidiq, Faisal Shofia Hilabi, Shofa Sopian, Jajang Sri Wahyuni Sudrajat, Deden Renhad Sugiyanto, Yanto Sunarya, Edwin Yohanes Syafana, Vinka Tamala, Evi Tri Rudiyati, Eli Tukino, Tukino Zulfiana, Rizka