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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Semantik TELKOMNIKA (Telecommunication Computing Electronics and Control) PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic CommIT (Communication & Information Technology) Seminar Nasional Informatika (SEMNASIF) Register: Jurnal Ilmiah Teknologi Sistem Informasi Creative Information Technology Journal SISFOTENIKA Bianglala Informatika : Jurnal Komputer dan Informatika Akademi Bina Sarana Informatika Yogyakarta Indonesian Journal on Software Engineering (IJSE) INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi Bina Insani ICT Journal Informatics for Educators and Professional : Journal of Informatics Jurnal Komputer Terapan JTERA (Jurnal Teknologi Rekayasa) Jurnal Ilmiah Matrik Conference SENATIK STT Adisutjipto Yogyakarta ILKOM Jurnal Ilmiah Jurnal Nasional Komputasi dan Teknologi Informasi Jurnal Penelitian dan Pengabdian Kepada Masyarakat UNSIQ Jurnal Informasi dan Komputer Aptisi Transactions on Management Aptisi Transactions on Technopreneurship (ATT) Informasi Interaktif CCIT (Creative Communication and Innovative Technology) Journal Building of Informatics, Technology and Science Progresif: Jurnal Ilmiah Komputer SENSITEK E-JURNAL JUSITI : Jurnal Sistem Informasi dan Teknologi Informasi Indonesian Journal of Business Intelligence (IJUBI) Journal of Robotics and Control (JRC) JATI (Jurnal Mahasiswa Teknik Informatika) Respati ICIT (Innovative Creative and Information Technology) Journal Journal Sensi: Strategic of Education in Information System CICES (Cyberpreneurship Innovative and Creative Exact and Social Science) Journal Cerita: Creative Education of Research in Information Technology and Artificial Informatics IJIIS: International Journal of Informatics and Information Systems Jurnal Informa: Jurnal Penelitian dan Pengabdian Masyarakat Jurnal Kajian Ilmiah JINAV: Journal of Information and Visualization Journal of Applied Data Sciences International Journal for Applied Information Management Jurnal Ekonomi dan Teknik Informatika Jurnal Ilmiah IT CIDA : Diseminasi Teknologi Informasi Jurnal Dinamika Informatika (JDI) Duta.com : Jurnal Ilmiah Teknologi Informasi dan Komunikasi Malcom: Indonesian Journal of Machine Learning and Computer Science Universal Raharja Community (URNITY Journal) IJOEM: Indonesian Journal of Elearning and Multimedia Prosiding SEMNAS INOTEK (Seminar Nasional Inovasi Teknologi) Blockchain Frontier Technology (BFRONT) Journal Collabits Journal of Digital Market and Digital Currency
Henderi .
Raharja University
Aerospace Engineering Humanities Automotive Engineering 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 Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Mechanical Engineering Neuroscience Social Sciences Transportation Other

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The Effect of The Prediction of The K-Nearest Neighbor Algorithm on Surviving COVID-19 Patients in Indonesia Martono, Aris; Henderi, Henderi; Maulani, Giandari
ILKOM Jurnal Ilmiah Vol 15, No 2 (2023)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v15i2.1234.240-249

Abstract

This study aims to measure the prediction of survival of covid-19 patients with the best algorithm based on RMSE(Root Mean Square Error). The Covid-19 pandemic has lasted from December 2019 until now and is full of uncertainty about when this pandemic will end, so this research was carried out. In this study, the knowledge discovery database method was used by extracting data sets from Covid-19 patients from March 2020 to March 2021 for each province in Indonesia (Dataset from Kawal Covid-19 SintaRistekbrin) to predict survival during this pandemic as measured by the best algorithms include k-NN (k-Nearest Neighbor), SVM (Support Vector Machine), and/or Deep Learning. The measurement results using cross-validation and the optimal number of folds is 3 in the form of RSME, showing that the k-NN algorithm is an algorithm with RSME 0.101 +/-0.23 where the error rate is the lowest compared to the two algorithms above. Therefore, the k-NN algorithm was chosen as the algorithm for the predictive measurement of surviving Covid-19 patients.
Expert System Application for Troubleshooting and Maintaining Epson L3110 Printer Henderi Henderi; Efana Rahwanto; Tri Wahyuningsih; Achmad Badrianto
PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Vol. 9 No. 1 (2021): Maret 2021
Publisher : LPPM Universitas Islam 45 Bekasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33558/piksel.v9i1.2429

Abstract

Many organizations are making changes by using information technology to support their business activities. Routine and uncomplicated activities tend to be carried out supported by computer-based applications. These activities include diagnosing damage and performing maintenance on the printer. This study aims to develop an Excel system application for fault diagnosis and maintenance of printers. The expert system printer damage diagnosis application in this study was developed based on a knowledge base. The research was conducted through the stages of needs analysis, design, implementation and testing. The test results show that the application developed is able to diagnose and display printer defects and provide solutions and fixed it.
Automatic diagnosis of rice plant diseases using VGG-16 and computer vision Al-Bahra Al-Bahra; Henderi Henderi; Nur Azizah; Muhammad Hudzaifah Nasrullah; Didik Setiyadi
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 23, No 6: December 2025
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v23i6.26975

Abstract

Pathogens are organisms that cause disease in plants. In the case of rice, these pathogens can include fungi, bacteria, nematodes, protozoa, and viruses. This study aims to investigate rice plant diseases using a hybrid system that employs the visual geometry group-16 (VGG-16) architecture and computer vision techniques, alongside various optimization algorithms and hyperparameters. We utilize the convolutional neural network (CNN) architecture of VGG-16 for feature extraction, implementing a process known as transfer learning. Additionally, this research compares different optimization algorithms with the VGG-16 model to identify the most effective optimization for the CNN architecture applied to the tested dataset. The main contribution of this study is the development of a model for identifying rice plant diseases based on data collected using VGG-16 for feature extraction and neural networks for classification with specific parameters. Our findings indicate that the best optimization algorithm is stochastic gradient descent (SGD) with momentum, achieving training and validation loss results of 0.173 and 0.168, respectively. Furthermore, the training and validation accuracies were 0.95 and 0.957. The model’s performance metrics include an accuracy of 95.75, precision of 95.75, recall of 95.75, and an F1-score of 95.73.
Prediction of heart disease using random forest algorithm, support vector machine, and neural network Didik Setiyadi; Henderi Henderi; Anrie Suryaningrat; Rulin Swastika; Saludin Saludin; Muhamad Malik Mutoffar; Imam Yunianto
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 23, No 1: February 2025
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v23i1.25341

Abstract

The heart is a vital organ responsible for pumping blood throughout the human body. Machine learning has become an increasingly important tool in medical forecasting, improving diagnostic accuracy and reducing human errors. This study focuses on detecting heart disease using machine learning algorithms. It aims to compare the performance of three key algorithms random forest (RF), support vector machine (SVM), and neural networks (NN), in predicting heart disease. Using a patient dataset with both nominal and numeric attributes, record mining techniques were applied through Orange software. The target classes indicated the absence (0) or presence (1) of heart disorders. The evaluation was based on the prediction accuracy of each algorithm. Results show that SVM achieved the highest accuracy, with a rate of 85%, outperforming RF and NN. The findings suggest that the SVM algorithm is a reliable tool for heart disease prediction, helping reduce diagnostic errors and improve medical decision-making.
Hybrid Learning for Automated E-commerce Churn Prediction with XGBoost and SHAP Henry, Amir Acalapati; Arribathi, Abdul Hamid; Henderi, Henderi
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 6 No. 3 (2026): MALCOM July 2026
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v6i3.2833

Abstract

Customer retention is a critical challenge in the e-commerce industry, yet many platforms frequently suffer from a lack of explicit labels to identify potential defectors (churn). This research proposes a hybrid unsupervised-supervised learning framework to perform automated churn classification using the high-dimensional Olist Brazilian E-commerce dataset. The first stage employs K-Means Clustering to objectively generate churn labels based on Recency, Frequency, and Monetary (RFM) features. The second stage applies an Extreme Gradient Boosting (XGBoost) model, augmented by the Synthetic Minority Over-sampling Technique (SMOTE), to address the inherent class imbalance typical of transactional data. Experimental results demonstrate that the proposed model achieved a robust accuracy of 72% with a churn recall rate of 69%. Interpretability analysis using SHapley Additive exPlanations (SHAP) revealed that delivery duration and customer review scores are the most dominant predictors of churn, significantly outweighing financial metrics. These findings contribute a novel integration of automated labeling and model transparency, enabling e-commerce managers to implement proactive, data-driven customer retention strategies.
EVALUASI TINGKAT KEMATANGAN SPBE DI DISPERINDAG KABUPATEN BANJAR M Rizeki Yuda Saputra; Wing Wahyu Winarno; Henderi Henderi
Indonesian Journal of Business Intelligence (IJUBI) Vol 3 No 1 (2020): Indonesian Journal of Business Intelligence (IJUBI)
Publisher : Universitas Alma Ata

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21927/ijubi.v3i1.1188

Abstract

AbstrakPenelitian ini dilakukan untuk mengetahui capaian kemajuan dan memberikan saran untuk  pelaksanaan domain layanan SPBE di Dinas Perindustrian dan Perdagangan Kabupaten Banjar. Struktur penilaian SPBE dikhususkan pada Domain 3 Layanan SPBE tingkat kematangan pada kapabilitas fungsi dan menggunakan metode  CMM/CMMI Development versi 1.3.Perhitungan Indeks domain Layanan SPBE dilakukan berdasarkan hasil pengolahan data dari 12 responden pada kuesioner SPBE, kemudian Roamap CMMI dipetakan berdasarkan domain layanan SPBE yang memiliki nilai kesesuaian paling tinggi digunakan sebagai roadmap yang palin cocok, yang dalam penelitian ini adalah Process Roadmap yang kemudian diukur tingkat kematangan tiap Process Area- nya (Process area Organizational Process Focus, Process area Organizational Focus Definition, Process area Measurement and Analysis, Process area Causal Analysis and Resolution, Process area Process and Product Quality Assurance).Rekomendasi diberikan berdasarkan hasil perhitungan tingkat kematangan dari setiap Process Area agar saran yang diberikan dapat digunakan sebagai langkah perbaikan yang tepat dan berkelanjutan bagi instansi terkait.
Improving the Quality of Decision Making in Educational Institutions with Business Intelligence Diar Eka Purnama; Abdul Hamid Arribathi; Nur Azizah; Henderi Henderi
Journal Collabits Vol. 3 No. 2 (2026)
Publisher : Journal Collabits

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/collabits.v3i2.39388

Abstract

Effective decision-making in educational institutions relies heavily on the availability of accurate, timely, and easy-to-interpret data. However, many schools and colleges still face challenges in managing data spread across various systems, so decisions are often made based on assumptions or partial information. This study aims to analyze the use of Business Intelligence (BI) to improve the quality of decision-making in educational institutions. In this study, a qualitative approach is used with case studies in three educational institutions that have implemented the BI system. The procedure in this study is by: collecting data through interviews with institutional leaders, observation of the use of analytical dashboards, and analysis of policy documents. The results show that BI enables real-time data visualization related to academic performance, resource allocation, and stakeholder satisfaction, thereby accelerating and strengthening strategic and operational decision-making processes. In addition, BI encourages a data-driven culture that increases transparency and accountability. However, the success of BI's implementation is greatly influenced by the readiness of digital infrastructure, human resource competence, and the commitment of top management. This study concludes that BI is an effective strategic tool for education governance transformation, as long as it is holistically integrated into the institutional management system. These findings provide practical recommendations for education leaders seeking to improve the quality of decisions through the use of analytics technology.
Co-Authors Abas Sunarya Abas, Ashardi bin Abda Abda Abdul Hamid Arribathi Abdul Hamid Arribathi Achmad Badrianto Achmad Udin Zailani Adi Setiawan Aditya Prihantara Agung Yudo Ardianto Ahmad Sidik Ainiyatul Maghfiroh Al Khudhorie, Fahmie Al- Bahra Aldi Destaryana Alfiah, Fifit Ali Djamhuri Alwan Hibatullah Andang Wijanarko Andrian Saputra Andrie Prajanueri Kristianto Anindita Septiarini, Anindita Anrie Suryaningrat Ar Ridho Gusti Ari Ari Suhartanto Ari Suhartanto Arie Afriyoga Arief Setyanto Arif, Achmad Yusron Arifin, Rita Wahyu Ary Budi Warsito Asep Saefullah Asro Asro Auliasari, Siti Risma B. Herawan Hayadi Badrianto, Achmad Bambang Soedijono W.A Bambang Soedijono, Bambang Bambang Soedjiono W.A Bangun Mukti Prasetyo Bin Ladjamudin, Al Bahra Bramantyo Yudi Wardhana Budiarto, Mukti Destyanto, Febrian Devana, Viola Tashya Devi Rositawati Dewi, Deshinta Arrova Diar Eka Purnama Didi Rahmat Didik Setiyadi Didik Setiyadi Didik Setiyadi Dwinda Etika Profesi Efana Rahwanto Efana Rahwanto Ema Utami Euis Nurninawati Euis Siti Nur Aisyah Fata Nidaul Khasanah FAUZAN, AKMAL Fazlul Rahman Fitria Dewi, Alda Galuh Fitria Nursetianingsih Frama Yenti Giandari Maulani, Giandari Gugun Gunawan Gunawan, Deddy Gutama, Deden Hardan Hady, Hamdy Haekal Simangunsong, Fikri Muhammad Hamdani Hamdani Hamdani Hamdani Hari Agustiyo Hatta, Heliza Rahmania Henry, Amir Acalapati Husein Muhammad Fahrezy Husni Teja Sukmana I Ketut Gunawan Ignatius Agus Supriyono Ilham Hizbuloh Imam Yunianto Ina Sholihah Widiati, Ina Sholihah Indri Handayani Indri Handayani Ira Tyas Ningrum Irwan Sembiring Ismail, Abdul Azim Bin Iwan Setyawan Jahiri, Muhamad Jahri, Muhamad Jamaludin, Dieng Asep Julia Kurniasih Junaidi Junaidi Junaidi Junaidi Kartawinata, Dea Karunia Suci Lestari Kasim, Shahreen Binti Khairunnisak Nur Isnaini Kholil, Moenawar Khurotul Aeni, Khurotul Kurniawan, Tri Basuki Kusrini Ladjamudin, Al-Bahra bin Ladjamudin, AlBahra Bin M Rizeki Yuda Saputra M Said Hasibuan M. Rizeki Yuda Saputra M. Suyanto, M. Maimunah Maimunah Maimunah, Maimunah Martono, Aris Mashal Alqudah Maulidina, Muhammad Muflih Meta Amalia Dewi Millah, Shofiyul Misinem, Misinem Moh Muhtarom Mohammad Hairidzulhi Mohammad Santosa Mulyo Diningrat Muhamad Hendri Muhamad Malik Mutoffar Muhamad Yusup Muhammad Hudzaifah Nasrullah Mujianto, Ahmad Heru Mulyana, Muhamad Mulyati Mulyati Mulyati Mulyati Muntasir, Ibnu Muthiah Abda Azizah, Muthiah Abda Nathan, Yogeswaran Neno, Friden Elefri Nia Kusniawati Ningrum, Rahma Farah Novi Cholisoh Nugraha, Rizal Fitrah Nur Aisyah, Euis Siti Nur Azizah Nurcahyanie, Yunia Padeli Padeli Periasamy, Jeyarani Pipin Romansyah Po Abas Sunarya Prabowo Pudjo Widodo Pradana, Restu Adi Praditya Aliftiar Pramono, Galih Puspitasari, Novianti Putri, Cheetah Savana Putri, Dian Mustika Qory Oktisa Aulia Rafika, Ageng Setiani Rahmat, Didi Rahwanto, Efana Raja, Berisno Hendro Pardamean Manik Randy Andrian Rani Putri Merliasari Rano Kurniawan Riki Mardiana Rita Wahyuni Arifin Rony Heri Irawan Ruli Supriati, Ruli Rulin Swastika Safar Dwi Kurniawan Saludin Saludin Saputra, M Rizeki Yuda Saputra, M. Rizeki Yuda Setianto, Yuni Ambar Singh, Harprith Kaur Rajinder Siti Khodijah Siti Ria Zuliana, Siti Ria Sofiana, Sofa Solikin, Mokhamad Sri Rahayu Sudaryono Sudaryono Sudaryono Sudaryono Sugeng Santoso Suharto - Sulaiman, Agus Sutami, Sutami Suyatno Suyatno Swastika, Rulin Syahrial Shaddiq Taufik Hidayat Theopillus J. H. Wellem Toga Parlindungan Silaen Tri Wahyuningsih Tri Wahyuningsih Tri Wahyuningsih Tuah, Nooralisa Mohd Tubagus Ahmad Harja Kusuma Umdatur Rosyidah Uning Lestari Untung Rahardja W, Bambang Soedijono Winarno Winarno Winarno Winarno Wing Wahyu Winarno Yeni Nuraeni Yulika Ayu Rantama Yuni Ambar S Yunia Riska Anggrahini Yusuf, Inayatul Izzati Diana Zakaria, Mohd Zaki Zcull, Harph