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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) 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 Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Mechanical Engineering Neuroscience Social Sciences Transportation Other

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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.
Incorporate Transformer-Based Models for Anomaly Detection Deshinta Arrova Dewi; Harprith Kaur Rajinder Singh; Jeyarani Periasamy; Tri Basuki Kurniawan; Henderi Henderi; M. Said Hasibuan; Yogeswaran Nathan
Journal of Applied Data Sciences Vol 6, No 3: September 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i3.762

Abstract

This paper explores the effectiveness of Transformer-based models, specifically the Time-Series Transformer (TST) and Temporal Fusion Transformer (TFT), for anomaly detection in streaming data. We review related work on anomaly detection models, highlighting traditional methods' limitations in speed, accuracy, and scalability. While LSTM Autoencoders are known for their ability to capture temporal patterns, they suffer from high memory consumption and slower inference times. Though efficient in terms of memory usage, the Matrix Profile provides lower performance in detecting anomalies. To address these challenges, we propose using Transformer-based models, which leverage the self-attention mechanism to capture long-range dependencies in data, process sequences in parallel, and achieve superior performance in both accuracy and efficiency. Our experiments show that TFT outperforms the other models with an F1-score of 0.92 and a Precision-Recall AUC of 0.71, demonstrating significant improvements in anomaly detection. The TST model also shows competitive performance with an F1-score of 0.88 and Precision-Recall AUC of 0.68, offering a more efficient alternative to LSTMs. The results underscore that Transformer models, particularly TST and TFT, provide a robust solution for anomaly detection in real-time applications, offering improved performance, faster inference times, and lower memory usage than traditional models. In conclusion, Transformer-based models stand out as the most effective and scalable solution for large-scale, real-time anomaly detection in streaming time-series data, paving the way for their broader application across various industries. Future work will further focus on optimizing these models and exploring hybrid approaches to enhance detection capabilities and real-time performance.
Detecting Gender-Based Violence Discourse Using Deep Learning: A CNN-LSTM Hybrid Model Approach Tri Basuki Kurniawan; Deshinta Arrova Dewi; Henderi Henderi; M. Said Hasibuan; Mohd Zaki Zakaria; Abdul Azim Bin Ismail
Journal of Applied Data Sciences Vol 6, No 3: September 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i3.761

Abstract

Gender-Based Violence (GBV) is a critical social issue impacting millions worldwide. Social media discussions offer valuable insights into public awareness, sentiment, and advocacy, yet manually analyzing such vast textual data is highly challenging. Traditional text classification methods often struggle with contextual understanding and multi-class categorization, making it difficult to accurately identify discussions on Sexual Violence, Physical Violence, and other topics. To address this, the present study proposes a hybrid deep learning approach combining Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks. CNN is utilized for extracting key linguistic features, while LSTM enhances the classification process by maintaining sequential dependencies. This hybrid CNN+LSTM model is evaluated against standalone CNN and LSTM models to assess its performance in classifying GBV-related tweets. The dataset was sourced from Kaggle, containing real-world Twitter discussions on GBV. Experimental results demonstrate that the hybrid model surpasses both CNN and LSTM models, achieving an accuracy of 89.6%, precision of 88.4%, recall of 89.1%, and F1-score of 88.7%. Confusion matrix and ROC curve analyses further confirm the hybrid model’s superior performance, correctly identifying Sexual Violence (82%), Physical Violence (15%), and Other (3%) cases with reduced misclassification rates. These results suggest that combining CNN’s feature extraction with LSTM’s contextual learning provides a more balanced and effective classification model for GBV-related text. This work supports the development of AI-based tools for social media monitoring, policy-making, and advocacy, helping stakeholders better understand and respond to GBV discussions. Future research could explore transformer-based models like BERT and real-time classification applications to further improve performance.
Utilizing Sentiment Analysis for Reflect and Improve Education in Indonesia Henderi Henderi; Asro Asro; Agus Sulaiman; Tri Basuki Kurniawan; Deshinta Arrova Dewi; Mashal AlQudah
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.v6i1.527

Abstract

This study explores the potential of sentiment analysis in providing valuable insights into education in Indonesia based on comments from the YouTube platform. Utilizing the Naive Bayes Classifier method, this research analyzed 13,386 processed comments out of 17,920 original comments. The results show that 53.8% of comments were negative, while 28.5% were positive, and 17.7% were neutral, reflecting diverse perspectives on existing educational issues. The Accuracy of this model reached up to 72.51% with testing on various sample sizes (10%-30%), indicating the model's effectiveness in identifying sentiments. Although the model tends to classify comments as unfavorable, this opens opportunities for introspection and improvement within the educational system. Further analysis with a word cloud revealed dominant keywords, indicating areas that require more attention in public discussions about education. By leveraging this sentiment analysis, the study offers practical and valuable guidance for policymakers to reflect on and enhance educational strategies and policies in Indonesia. This research measures public reactions and aims to foster more constructive and inclusive discussions about the sustainable development of education in Indonesia.
Unsupervised Learning Methods for Topic Extraction and Modeling in Large-scale Text Corpora using LSA and LDA Henderi Henderi; B Herawan Hayadi; Sofa Sofiana; Padeli Padeli; Didik Setiyadi
Journal of Applied Data Sciences Vol 4, No 3: SEPTEMBER 2023
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v4i3.102

Abstract

This research compares unsupervised learning methods in topic extraction and modeling in large-scale text corpora. The methods used are Singular Value Decomposition (SVD) and Latent Dirichlet Allocation (LDA). SVD is used to extract important features through term-document matrix decomposition, while LDA identifies hidden topics based on the probability distribution of words. The research involves data collection, data exploratory analysis (EDA), topic extraction using SVD, data preprocessing, and topic extraction using LDA. The data used were large-scale text corpora. Data explorative analysis was conducted to understand the characteristics and structure of text corpora before topic extraction was performed. SVD and LDA were used to identify the main topics in the text corpora. The results showed that SVD and LDA were successful in topic extraction and modeling of large-scale text corpora. SVD reveals cohesive patterns and thematically related topics. LDA identifies hidden topics based on the probability distribution of words. These findings have important implications in text processing and analysis. The resulting topic representations can be used for information mining, document categorization, and more in-depth text analysis. The use of SVD and LDA in topic extraction and modeling of large-scale text corpora provides valuable insights in text analysis. However, this research has limitations. The success of the methods depends on the quality and representativeness of the text corpora. Topic interpretation still requires further understanding and analysis. Future research can develop methods and techniques to improve the accuracy and efficiency of topic extraction and text corpora modeling.
Co-Authors Abas Sunarya Abas, Ashardi bin Abda Abda Abdul Azim Bin Ismail Abdul Hamid Arribathi Abdul Hamid Arribathi Achmad Badrianto Achmad Udin Zailani Adi Setiawan Aditya Prihantara Agung Yudo Ardianto Agus Sulaiman 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 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 Deshinta Arrova Dewi Deshinta Arrova Dewi Destyanto, Febrian Devana, Viola Tashya Devi Rositawati Dewi, Deshinta Arrova Diar Eka Purnama Didi Rahmat Didik Setiyadi 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 Hari Agustiyo Harprith Kaur Rajinder Singh 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 Jeyarani Periasamy Julia Kurniasih Junaidi Junaidi Junaidi Junaidi Kartawinata, Dea Karunia Suci Lestari 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. Said Hasibuan M. Suyanto, M. Maimunah Maimunah Maimunah Marlina, Uning Martono, Aris Mashal AlQudah Mashal Alqudah Maulidina, Muhammad Muflih Meta Amalia Dewi Millah, Shofiyul Moh Muhtarom Mohammad Hairidzulhi Mohammad Santosa Mulyo Diningrat Mohd Zaki Zakaria 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 Sofa Sofiana 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 Basuki Kurniawan Tri Basuki Kurniawan Tri Basuki Kurniawan Tri Wahyuningsih Tri Wahyuningsih Tri wahyuningsih Tri Wahyuningsih Tuah, Nooralisa Mohd Tubagus Ahmad Harja Kusuma Umdatur Rosyidah Untung Rahardja W, Bambang Soedijono Winarno * **, Winarno * Winarno Winarno Winarno Winarno Wing Wahyu Winarno Yeni Nuraeni Yogeswaran Nathan Yulika Ayu Rantama Yuni Ambar S Yunia Riska Anggrahini Yusuf, Inayatul Izzati Diana Zakaria, Mohd Zaki Zcull, Harph