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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) KLIK: Kajian Ilmiah Informatika dan Komputer J-SAKTI (Jurnal Sains Komputer dan Informatika) 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
Unknown Affiliation
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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Analisis Sentimen E-Learning X Terhadap Antarmuka Pengguna Menggunakan Kombinasi Multinomial Naive Bayes Dan Pendekatan Design Thinking Huda, Baenil; Sembiring, Irwan; Setiawan, Iwan; Manongga, Danny; Purnomo, Hindriyanto Dwi; Hendry, Hendry; Fauzi, Ahmad; Lia Hananto, April; Tukino, Tukino
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 11 No 4: Agustus 2024
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.1147686

Abstract

Penelitian ini bertujuan untuk menganalisis sentimen pengguna terhadap antarmuka e-learning X menggunakan kombinasi Multinomial Naive Bayes dan pendekatan Design Thinking. Permasalahan yang dihadapi adalah banyaknya feedback negatif terkait antarmuka pengguna yang dianggap kurang intuitif. Data sentimen dari ulasan pengguna diklasifikasikan menggunakan algoritma Multinomial Naive Bayes, sementara Design Thinking digunakan untuk merancang solusi antarmuka yang lebih user-friendly. Hasilnya menunjukkan bahwa metode ini efektif meningkatkan sentimen positif pengguna, dengan perbaikan signifikan dalam pengalaman dan kepuasan pengguna terhadap antarmuka e-learning X, Serta rekomendasi untuk pengembangan aplikasi e-learning.   Abstract   This research aims to analyze user sentiment towards the e-learning interface X using a combination of Multinomial Naive Bayes and Design Thinking approaches. The problem faced was the large number of negative feedback regarding the user interface which was considered less intuitive. Sentiment data from user reviews is classified using the Multinomial Naive Bayes algorithm, while Design Thinking is used to design more user-friendly interface solutions. The results show that this method is effective in increasing positive user sentiment, with significant improvements in user experience and satisfaction with the X e-learning interface As well as recommendations for developing e-learning applications.
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 : STMIK 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.
ANALISIS KEPUASAN MASYARAKAT TERHADAP PELAYANAN ONLINE KEPENDUDUKAN MELALUI APLIKASI EDUKCAPIL MENGGUNAKAN METODE CRM Rismoko, Rismoko; Hilabi, Shofa Shofia; Huda, Baenil
INTECOMS: Journal of Information Technology and Computer Science Vol 7 No 3 (2024): INTECOMS: Journal of Information Technology and Computer Science
Publisher : Institut Penelitian Matematika, Komputer, Keperawatan, Pendidikan dan Ekonomi (IPM2KPE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31539/intecoms.v7i3.9815

Abstract

Tujuan analisis kepuasan masyarakat adalah untuk mengukur sejauh mana kualitas layanan kependudukan online dapat memenuhi harapan dan kebutuhan masyarakat. Hal ini termasuk mengevaluasi daya tanggap, kecepatan, dan kejelasan informasi yang diberikan. Sistem Informasi CRM (Customer Relationship Management) yang merupakan suatu sistem informasi yang terintegrasi yang digunakan untuk merencanakan, menjadwalkan, dan mengendalikan aktivitas-aktivitas pra pelayanan dan pasca pelayanan dalam sebuah organisasi untuk meningkatkan kepuasan pelanggan dengan tujuan sebagai solusi pelayanannya yang didukung oleh teknologi informasi untuk dapat memberikan berbagai kemudahan dan peningkatan kualitas layanan kepada masyarakat mulai dari sample request, customer trial, customer complaint, customer grouping sampai customer satisfaction. Hasil penelitian menghasilkan kepuasaan masyarakat melalui pelayanan online edukcapil yang disedikan disdukcapil kabupaten karawang diperlukan pengembangan sistem yang lebih baik Kata Kunci: Perkembangan Teknologi Informasi, Pelayanan Publik, Pelayanan Online, Kependudukan, Kepuasan Masyarakat.
Arsitektur Enterprise Aplikasi SIP Menggunakan Kerangka Kerja Zachman Sugiyanto, Yanto; Shofia Hilabi, Shofa; Huda, Baenil
Jurnal Informatika dan Teknologi Komputer (J-ICOM) Vol 5 No 1 (2024): Jurnal Informatika dan Teknologi Komputer ( JICOM)
Publisher : Universitas Samudra

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55377/j-icom.v5i1.9665

Abstract

The Pension Information System (SIP) is a digital platform specifically designed for comprehensive and transparent management of pension information. In modern administration, administrative efficiency and transparency are very important to improve the quality of public services. Therefore, the development of an appropriate pension information system (SIP) has a strategic role in increasing the productivity and work efficiency of civil servants. Enterprise application architecture has become an important element in the development of complex information systems. In this research, we investigate the application of the Zachman framework to the architectural design of enterprise pension information system (LIS) applications. Research methods include documentary analysis and interviews with experts in the field of software architecture. Our research results show that the application of the Zachman framework provides an organized and clear structure to the architectural design of enterprise pension information system (LIS) applications. By considering the various aspects provided by the Zachman framework, the architectural design process can be carried out systematically and efficiently. It is hoped that this research can contribute to the development of a company application architecture design methodology, especially those related to the implementation of pension information systems (SIP). The findings of this research also encourage further research regarding the application of new technology to support information system integration at the agency level.
Pengelompokkan Data Obat-Obatan Pada Pelayanan Kesehatan Menggunakan Algoritma K-Means Clustering Saptiani, Anita; Huda, Baenil; Novalia, Elfina; Purba, Arif Budimansyah
JURSIMA Vol 10 No 3 (2022): Jursima Vol.10 No.3
Publisher : INSTITUT TEKNOLOGI DAN BISNIS INDOBARU NASIONAL

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47024/js.v10i3.510

Abstract

ABSTRACT In planning accurate drug needs, the drug procurement becomes more effetive and efficient, so that it can be available with the type and amount that is needed. Clustering data mining can be used to analyze drug usage, drug palnning and management at the health center. The method that will be applied is the clustering method on drug data using the K-Means algorithm which can divide data into clusters so that data has similarities will be grouped into one group and different data will be combined in other groups. The purpose of this study was to classify drug data at the Karangsambung health center which could be used as a reference for decision making in planning and supplying drugs at the Karangsambung health center. The results of this study are classifying the level of drugs use at the Karangsambung health center, where the data was taken from 2019 to 2022. The resulting data was grouped into 3 clusters, which later collected high, medium and low usage.
Classification of Starling Images Using a Bayesian Network Hananto, April Lia; Rahman, Aviv Yuniar; Paryono, Tukino; Priyatna, Bayu; Hananto, Agustia; Huda, Baenil
Journal of Applied Data Sciences Vol 6, No 1: JANUARY 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i1.423

Abstract

The classification of starling species is vital for biodiversity conservation, especially as some species are endangered. This research investigates the effectiveness of the Bayesian Network (BayesNet) for classifying starling species and compares its performance with Artificial Neural Networks (ANN) and Naive Bayes models. The dataset comprises 300 images of five starling species—Bali, Rio, Moon, Kebo, and Uret—captured under controlled conditions. Feature extraction focused on color, texture, and shape, while data augmentation through slight image rotations was applied to enhance model generalization. The BayesNet model achieved an accuracy of 96.29% using a 90:10 training-to-testing split, outperforming ANN (90.74%) and Naive Bayes variants. Precision, recall, F1-score, and AUC-ROC values further validated the robustness of the BayesNet model, with precision at 0.90, recall at 0.91, F1-score at 0.92, and AUC-ROC at 0.95. These results demonstrate the superior performance of multi-feature Bayesian Networks in starling classification compared to other machine learning models. The novelty of this study lies in its application of a probabilistic approach using Bayesian Networks, which enhances interpretability and performance, especially in scenarios with limited data. Future work may explore additional feature sets and advanced machine learning models to further improve classification accuracy and robustness.
Identifying Regional Patterns of Poverty in Indonesia: a Clustering Approach Using K-Means Wahyuni, Sri; Hananto, Agustia; Huda, Baenil; Apriani, Fitria; Tukino, Tukino
International Journal of Computer and Information System (IJCIS) Vol 6, No 1 (2025): IJCIS : Vol 6 - Issue 1 - 2025
Publisher : Institut Teknologi Bisnis AAS Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29040/ijcis.v6i1.218

Abstract

Poverty in Indonesia remains a major challenge, with significant levels of inequality between provinces. This study applies the K-Means clustering method to analyze poverty distribution patterns in 38 provinces in Indonesia, using data on the percentage of poor people from 2010 to 2024. With this approach, provinces are grouped into three main clusters: low, medium, and high, based on the average poverty rate. The low cluster includes areas with poverty rates below 10%, while the medium and high clusters indicate poverty levels that require more specific policies. The evaluation results show a silhouette score of 0.613, indicating that this grouping is quite good but can still be improved. This study offers important insights to support more targeted and effective policies, especially in achieving Sustainable Development Goal (SDG) 1: Eradicating Poverty.
Analisis Sentimen Pengguna terhadap Aplikasi Lalamove dengan Perbandingan Algoritma Support Vector Machine dan Naive Bayes Nurajizah, Dhea; Hilabi, Shofa Shofia; Hananto, Agustia; Huda, Baenil
Journal of Information System Research (JOSH) Vol 6 No 3 (2025): April 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v6i3.7124

Abstract

The development of digital technology has brought significant changes to the logistics and transportation sector. On-demand delivery applications such as Lalamove are a solution for users who need fast and efficient services. However, the existence of various user reviews both positive and negative indicates a difference in experience that needs to be analyzed. This study aims to evaluate user perceptions of the Lalamove application by comparing the effectiveness of Support Vector Machine (SVM) and Naive Bayes algorithms in sentiment classification. The data used in this study includes 10,000 user reviews obtained through scraping techniques from the Google Play Store. After going through the data preprocessing stage, the analysis is performed using TF-IDF method as feature extraction and the model performance evaluation is performed based on accuracy, precision, recall, and F1-score metrics. Sentiment classification in this study was performed in two categories, namely positive and negative (binary) sentiment, without considering the neutral category. The results show that the SVM algorithm has a high accuracy of 87% compared to Naive Bayes which only reaches 83%. This research provides an understanding for application developers in improving service quality based on user sentiment analysis.
Analisis Sentimen Aplikasi Bank Digital Pada Google Play Store Menggunakan Algoritma Naive Bayes Arkan Hilman Hakim; Hananto, April Lia; Nurapriani, Fitria; Huda, Baenil
Journal of Informatics and Communication Technology (JICT) Vol. 7 No. 1 (2025)
Publisher : PPM Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52661/jict.v7i1.405

Abstract

Pesatnya perkembangan teknologi perbankan digital telah mendorong munculnya beragam aplikasi perbankan yang tersedia di Google Play Store. Ulasan pengguna terhadap aplikasi-aplikasi ini menjadi sumber informasi yang berharga untuk menilai tingkat kepuasan mereka, yang kemudian dapat dianalisis melalui pendekatan sentiment analysis. Penelitian ini dilakukan untuk mengkaji kecenderungan sentimen pengguna terhadap beberapa aplikasi bank digital dengan memanfaatkan algoritma Naïve Bayes. Data yang dianalisis berasal dari 1.000 ulasan pengguna untuk masing-masing aplikasi, yaitu Seabank, Krom Bank, Bank Jago, Blu by BCA, dan Bank Saqu. Seluruh proses analisis dan pengolahan data dilakukan menggunakan platform Google Colab, dengan menerapkan metode Multinomial Naïve Bayes (MNB) untuk mengklasifikasikan sentimen ke dalam dua kategori, yaitu positif dan negatif. Hasil penelitian mengungkapkan bahwa Seabank menunjukkan performa tertinggi, dengan accuracy sebesar 94%, precision 93%, recall 100%, dan F1-score 97%, serta total 945 ulasan positif dan 55 ulasan negatif. Temuan ini memperlihatkan bahwa analisis sentimen dapat memberikan masukan yang bernilai bagi pengembang untuk meningkatkan kualitas aplikasi, sekaligus menjadi panduan bagi pengguna dalam memilih layanan perbankan digital yang sesuai dengan preferensi kebutuhan.
Analisis Sentimen User Experience Menggunakan Naive Bayes dan Design Thinking pada Aplikasi SIPT Fauzi, Muhamad Helmi; Huda, Baenil; Novalia, Elfina
REMIK: Riset dan E-Jurnal Manajemen Informatika Komputer Vol. 9 No. 2 (2025): Volume 9 Nomor 2 April 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/remik.v9i2.14712

Abstract

Sistem Informasi Perguruan Tinggi Universitas Buana Perjuangan Karawang (SIPT UBP Karawang) merupakan aplikasi yang dirancang untuk mempermudah mahasiswa dalam mengelola administrasi akademik, seperti melihat nilai, pembayaran UKT, dan informasi perkuliahan. Berdasarkan pengalaman pengguna ditemukan permasalahan pada tampilan antarmuka, khususnya halaman dashboard yang dinilai kurang intuitif. Penelitian ini bertujuan untuk menganalisis sentimen pengguna terhadap aplikasi SIPT UBP Karawang menggunakan algoritma Naive Bayes, serta merancang solusi perbaikan antarmuka dengan pendekatan Design Thinking. Data yang dikumpulkan sebanyak 502 komentar pengguna aplikasi, setelah tahap preprocessing menjadi 406 data set komentar aplikasi. Hasil dari klasifikasi sentimen terdapat 236 sentimen positif dan 170 sentimen negatif. Visualisasi WordCloud pada komentar negatif menunjukan kata “dashboard” paling sering muncul, mengindikasikan titik masalah utama pada antarmuka. Proses klasifikasi menggunakan algoritma Naive Bayes menghasilkan akurasi sebesar 0.89% . Tampilan antarmuka didesain ulang agar lebih ramah pengguna menggunakan metode Design Thinking. Pengujian dilakukan menggunakan instrumen System Usability Scale (SUS), dan teknik Usability Testing. Skor rata-rata yang diperoleh adalah 81, jadi termasuk nilai A atau predikat sangat bagus. Penelitian ini menunjukkan bahwa hasil klasifikasi sentimen dengan metode Naive Bayes dan pendekatan Design Thinking efektif dalam melakukan identifikasi masalah dan menghasilkan solusi desain yang meningkatkan kepuasan pengguna terhadap aplikasi SIPT UBP Karawang.
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 Fauzi Ahmad Muda Fauzi, Muhamad Helmi Ferdiansyah, Indra Hananto, Agustia 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 Muhamad Bayu Aditya Pratama Muslih, Muhamad Novalia, Elfina Nurajizah, Dhea Nurapriani, Fitri 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 tukino, tukino yuwono, Fuad anwar Zulfiana, Rizka