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All Journal TEKNIK INFORMATIKA JURNAL SISTEM INFORMASI BISNIS Voteteknika (Vocational Teknik Elektronika dan Informatika) Elektron Jurnal Ilmiah Jurnal Sains dan Teknologi Bulletin of Electrical Engineering and Informatics Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Explore: Jurnal Sistem Informasi dan Telematika (Telekomunikasi, Multimedia dan Informatika) SITEKIN: Jurnal Sains, Teknologi dan Industri Jurnal Teknologi Informasi dan Ilmu Komputer Telematika Jurnal Edukasi dan Penelitian Informatika (JEPIN) Prosiding Semnastek JUITA : Jurnal Informatika Jurnas Nasional Teknologi dan Sistem Informasi Jurnal Ilmiah Rekayasa dan Manajemen Sistem Informasi Khazanah Informatika: Jurnal Ilmu Komputer dan Informatika Riau Journal of Computer Science JOIV : International Journal on Informatics Visualization Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) International Journal of Artificial Intelligence Research RABIT: Jurnal Teknologi dan Sistem Informasi Univrab INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi Jurnal Penelitian Pendidikan IPA (JPPIPA) Indonesian Journal of Artificial Intelligence and Data Mining JITK (Jurnal Ilmu Pengetahuan dan Komputer) Rang Teknik Journal ILKOM Jurnal Ilmiah MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Journal of Information Technology and Computer Engineering Jambura Journal of Informatics ComTech: Computer, Mathematics and Engineering Applications Jusikom: Jurnal Sistem Informasi Ilmu Komputer bit-Tech International Journal of Informatics and Computation Dinasti International Journal of Education Management and Social Science Systematics Jurnal Sistem Informasi dan Informatika (SIMIKA) Jurnal Sistim Informasi dan Teknologi Jurnal Informasi dan Teknologi Jurnal Informatika Ekonomi Bisnis Journal of Robotics and Control (JRC) Journal of Applied Engineering and Technological Science (JAETS) JATI (Jurnal Mahasiswa Teknik Informatika) Jurnal Ilmiah Manajemen Kesatuan Dinasti International Journal of Digital Business Management Indonesian Journal of Electrical Engineering and Computer Science JUKI : Jurnal Komputer dan Informatika Jurnal Perangkat Lunak Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Jurnal Teknik Informatika (JUTIF) Journal of Applied Data Sciences Jurnal Computer Science and Information Technology (CoSciTech) Journal of Applied Computer Science and Technology (JACOST) Jurnal Manajemen Sains Journal of Computer Scine and Information Technology Bulletin of Computer Science Research Jurnal Penelitian Inovatif Jurnal Ipteks Terapan : research of applied science and education Jurnal Pustaka AI : Pusat Akses Kajian Teknologi Artificial Intelligence Jurnal Teknoif Teknik Informatika Institut Teknologi Padang Jurnal Komtekinfo Jurnal Sistim Informasi dan Teknologi Jurnal Administrasi Sosial dan Humaniora (JASIORA) Innovative: Journal Of Social Science Research e-Jurnal Apresiasi Ekonomi Jurnal Informatika Ekonomi Bisnis SATIN - Sains dan Teknologi Informasi RJOCS (Riau Journal of Computer Science) SmartComp Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) JR : Jurnal Responsive Teknik Informatika Jurnal Responsive Teknik Informatika Lontar Komputer: Jurnal Ilmiah Teknologi Informasi Journal of Soft Computing Exploration
Sarjon Defit
Universitas Putra Indonesia YPTK Padang
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Customized Convolutional Neural Network for Glaucoma Detection in Retinal Fundus Images Fajrul Islami; Sumijan; Sarjon Defit
Jurnal Penelitian Pendidikan IPA Vol 10 No 8 (2024): August
Publisher : Postgraduate, University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jppipa.v10i8.7614

Abstract

Glaucoma is one of the leading causes of permanent blindness and remains a current challenge in the field of ophthalmology. This research aims to present a comprehensive investigation into the development and evaluation of new technology for glaucoma detection in retinal fundus images. The development and evaluation are presented on a customized architecture, using the Convolutional Neural Network (CNN) method. The proposed CNN architecture is designed to address the complex characteristics of glaucoma changes in the identification process. The research dataset consists of 506 retinal images categorized into 117 glaucoma images, 19 suspected glaucoma images, and 370 healthy images. Through our in-depth exploration, we conducted a careful analysis to uncover patterns and fundamental trends related to glaucoma-related features. During the training phase, the proposed CNN achieved outstanding average accuracy, sensitivity, and specificity values of 92.88%, 94.66%, and 89.31%, respectively. In the unseen test dataset, the model demonstrated competitive performance with an accuracy of 80.87%, sensitivity of 85.65%, and specificity of 71.26%. These findings emphasize the potential of the model as a reliable tool for glaucoma detection. The results indicate that the proposed method utilizing a customized CNN architecture is designed for glaucoma detection in retinal fundus images. The presented output results also hold promise for clinical relevance and can be considered an improvement in the care of retinal fundus patients.
Hybrid approach for identifying strategic promotional locations using k-means clustering and support vector machine classification Anisya Anisya; Brestina Gultom; Sarjon Defit
Journal of Soft Computing Exploration Vol. 7 No. 2 (2026): June 2026
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v7i2.45

Abstract

In the increasingly competitive landscape of higher education marketing, determining strategic promotional locations was essential to reaching prospective students effectively. This study proposed a hybrid machine learning framework combining K-Means clustering and Support Vector Machine (SVM) classification to identify high-potential areas for targeted promotional activities. The analysis used student enrolment data from 2021 to 2024, focusing on features such as city, province, and school origin. K-Means clustering was first applied to segment the data into three spatially and institutionally distinct clusters. These clusters were then used as pseudo-labels to train the SVM model, enabling the classification of new data points based on learned patterns. The model achieved a classification accuracy of 98%, with consistently high precision and recall across all clusters. Cluster interpretation revealed meaningful geographic and institutional differences that supported differentiated promotional strategies. Thematic map visualizations further enhanced the applicability of the model for geospatial decision-making. This study contributed to the development of data-driven, scalable, and interpretable solutions for location-based marketing. It also demonstrated the practical relevance of hybrid learning models in supporting strategic planning for educational institutions. Future work was suggested to incorporate additional socio-demographic variables and advanced ensemble methods to improve model robustness.
Optimizing Naive Bayes for Sentiment Analysis of M-Passport Reviews Using N-Gram and Synthetic Minority Over-sampling Technique Devia Kartika; Sarjon Defit
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 3 (2026): JUTIF Volume 7, Number 3, June 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.3.5853

Abstract

The diverse user perceptions and increasing number of negative reviews of the M-Passport application indicate the need for sentiment analysis-based evaluation to more accurately measure the quality of digital immigration services. This study aims to analyze user sentiment towards the M-Passport application using an optimized Naïve Bayes classification model. Review data was obtained through web scraping from various digital platforms and processed using text preprocessing, TF-IDF feature extraction, N-Gram representation, and the Synthetic Minority Over-sampling Technique (SMOTE) technique to address data representativeness. The proposed model classifies user reviews into positive, neutral, and negative sentiment categories. Test results show that optimization using N-Gram and SMOTE successfully improved model performance, with accuracy increasing from 61% to 77.51%, precision from 0.75 to 0.78, recall from 0.53 to 0.78, and F1-score from 0.50 to 0.77. These results demonstrate that the combination of feature engineering and data balancing can improve text context representation and sentiment classification stability across multiple classes. Furthermore, sentiment analysis successfully identified key factors contributing to user dissatisfaction, such as technical constraints, feature limitations, and application difficulty. These results demonstrate that the proposed approach is effective in supporting data-driven evaluation to improve the quality of digital immigration services.
Adaptive Integration of Optuna Optimization and Stacking Ensemble Learning for Automated Work Competency Classification Mutiana Pratiwi; Sarjon Defit; Muhammad Tajuddin
Journal of Applied Data Sciences Vol 7, No 2: May 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i2.1228

Abstract

Artificial intelligence and machine learning are increasingly used to automate analytical and decision processes, including the evaluation of human competencies. However, traditional models often face challenges in accuracy and generalization when applied to linguistic data from interviews. This study aims to develop a model that integrates Optuna optimization and stacking ensemble learning to enhance the accuracy and interpretability of competency classification. Interview transcript data were processed using natural language processing techniques such as cleaning, tokenization, case folding, stopword removal, and stemming to ensure textual consistency. The text was then transformed into numerical representations using term frequency inverse document frequency weighting. To handle class imbalance, the synthetic minority oversampling technique was employed. Optuna was applied to optimize the hyperparameters of base models, including support vector classifier, Naïve Bayes, random forest, gradient boosting, and XGBoost. These optimized models were combined through a stacking ensemble to form the final classifier. The proposed model achieved an accuracy of 94 percent and a precision of 95 percent with macro and weighted F1 scores of 0.94. The results demonstrate stable and balanced performance across all competency categories, including analytical thinking, initiating action, problem solving, and work standards. Comparative analysis with previous studies in sentiment analysis, medical diagnosis, and financial forecasting confirmed that the integration of Optuna and stacking produces more robust and generalizable outcomes. The integration of Optuna optimization and stacking ensemble learning effectively improves classification performance while maintaining interpretability. The model demonstrates strong potential for automated competency evaluation in recruitment and human resource analytics. This framework can be extended to other linguistic datasets to support transparent and data-driven decision-making in artificial intelligence applications.
An Integrated Text Analytics and Ensemble Machine Learning Framework for Fake Review Detection in Online Marketplaces Eka Praja Wiyata Mandala; Sarjon Defit; Gunadi Widi Nurcahyo
Journal of Applied Data Sciences Vol 7, No 2: May 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i2.1143

Abstract

The increasing prevalence of fake reviews on e-commerce platforms undermines consumer trust and affects purchasing decisions, particularly for local products by limited visibility such as those by West Sumatra, Indonesia. This study proposes a hybrid approach combining text analytics and machine learning to enhance the detection of fake reviews. Four classification models—Naive Bayes, Random Forest, Logistic Regression, and K-Nearest Neighbor—were tested on a dataset of 1,500 labeled product reviews. Among these models, Random Forest had the highest starting accuracy of 0.8533. To enhance it, we created a better algorithm called EKAHypeRFor (Enhanced Knowledge Augmentation of Hyperparameter Random Forest). This method uses simple feature engineering and careful tuning of settings by RandomizedSearchCV. The enhanced model reached an accuracy of 0.8778, which is 2.45% higher than the original. It also includes a real-time review sorting tool, making it easy to use on online shopping sites. Tests by a confusion matrix and feature importance drawn the model works well and is easy to understand. This method is simple, fast, and accurate, helping to make online product reviews more trustworthy for small and medium businesses in the area.
Penerapan Metode K-Means Clustering Dalam Pengelompokan Penyakit Pada Ayam Kampung Unggul Balibangtan Iqbal Afriyadi; Sarjon Defit; Sumijan Sumijan
Smart Comp :Jurnalnya Orang Pintar Komputer Vol 14, No 4 (2025): Smart Comp: Jurnalnya Orang Pintar Komputer
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/smartcomp.v14i4.8508

Abstract

Penyakit ayam saat ini merupakan salah satu ancaman terbesar pada sebuah peternakan ayam. Penyakit pada ayam bisa disebabkan oleh virus dan bakteri.  Ayam KUB merupakan salah satu jenis unggas yang dikembangkan oleh Badan Penelitian dan Pengembangan Pertanian Indonesia, dengan daya tahan tubuh yang baik dan produktivitas tinggi. Kendati demikian ayam KUB ini tetap rentan terhadap berbagai jenis penyakit yang dapat memengaruhi produktivitasnya. Pengelompokan penyakit pada ayam KUB penting untuk dilakukan guna mengidentifikasi pola serangan penyakit serta memberikan langkah preventif yang tepat bagi para peternak. Penelitian ini bertujuan untuk mengelompokkan penyakit yang menyerang Ayam Kampung Unggul Balitbangtan (KUB). Metode yang digunakan pada penelitian ini adalah penerapan machine learning dengan metode K-Means Clustering. Metode ini memiliki beberapa tahapan yaitu penyiapan data, normalisasi data, inisialisasi centroid, mengelompokkan data berdasarkan jarak terdekat, memperbarui centroid, iterasi sampai konvergensi, dan evaluasi hasil. Dataset yang diolah pada penelitian ini bersumber dari pengamatan langsung pada peternakan ayam ASA Farm Padang. Dataset yang digunakan dalam penelitian ini berjumlah 50 dataset yang berasal dari 50 ekor ayam KUB yang masuk kandang karantina pada peternakan tersebut. Pada penelitian ini menghasilkan kelompok penyakit ayam pada 3 kluster yaitu kluster 1 untuk ayam dengan penyakit gejala ringan dengan jumlah sebanyak 12 anggota, kluster 2 dengan penyakit gejala sedang dengan jumlah 14 anggota, dan kluster 3 dengan penyakit gejala tinggi sebanyak 24 anggota. Sehingga penelitian ini diharapkan dapat menjadi acuan bagi peternak, dokter hewan, peneliti selanjutnya atau pihak terkait dalam mengelompokan penyakit pada ayam kampung atau hewan ternak lainya.
Co-Authors Abdul Azis Said Abulwafa Muhammad Adawiyah, Quratih Ade, Ade Puspita Sari Adek Putri Adi Gunawan Adi Gunawan, Adi Adyanata Lubis Aflili Sari Afriosa Syawitri Agus Perdana Windarto Agustin, Riris Ahmad Zaki Ahmad Zaki Ahmad Zamsuri, Ahmad AHMADI Akbar, Muhamad Rafi Akbar, Syifa Chairunnissa Deliva Ali Ikhwan Alkhairi, Putrama Alvi Dwi Wahyuni Am, Andri Nofiar Amran Sitohang Anam, M Khairul Andema, Henky Andri Nofiar Angga Putra Juledi Anisya Anisya Anthony Anggrawan Arda Yunianta ardialis Ariandi, Vicky Arif Budiman Arif Budiman Arika Juwita Z Asri Hidayad Ayunda, Afifah Trista Bastola, Ramesh Billy Hendrik Bob Subhan Riza Bosker Sinaga Boy Sandy Dwi Nugraha.H Breinda, Engla Brestina Gultom Bufra, Fanny Septiani Chairun Nas Cyntia Trimulia Daeng Saputra Perdana Dahria, Muhammad Daniel Theodorus Dayla May Cytry Defi Pebriyanti Dendi Ferdinal Deno Yulfa Ardian Deti Karmanita Devia Kartika Devita, Retno Dhena Marichy Putri Dhio Saputra Dicky Novriansyah Dila, Rahmah Dinda Permata Sukma Dinul Akhiyar Dwi Utari Iswavigra Dwiki Aulia Fakhri Dwiprihatmo, Mohammad Reza Efendi, Akmar Efendi, Muhamad Efrizoni, Lusiana Eka Praja Wiyata Mandala Elda, Yusma Elfiswandi Elfiswandi eriwandi Fadillah, Riszki Fadlul Hamdi Faisal Roza Faizal Riza Faizal Riza Fajrul Islami Fajrul Islami Fanny Septiani Bufra Fatimah, Noor Fauzan Azim Fauzana, Rahmi Fauzi Erwis Febi Nur Salisah Febri Aldi Febri Hadi Febrina, Yerri Kurnia Firdaus Firdaus Firdaus, Muhammad Bambang Fitri Safnita Fitriani, Yetti Fristi Riandari Fuad El Khair Gaja, Rizqi Nusabbih Hidayatullah Ghea Paulina Suri Gunadi W Nurcahyo Gunadi Widi N. Gunadi Widi Nurcahyo Gunadi Widi Nurcahyo Guslendra Guslendra Guslendra, Guslendra Habdi, Habdi Hadiyanto, Tegas Halifia Hendri Hamsir hamsir Handika, Yola Tri Haris Kurniawan Hartati, Yuli Hasmaynelis Fitri Haviluddin Haviluddin Hazlita, H Hendrik, Billy Hendro Budiantoro Hengki Juliansa Henky Andema Hermanto Hidayad, Asri Honestya, Gabriela Huda, Ramzil Ikhbal Salam, Riyan Indah Savitri Hidayat Indhira, Sonia INTAN NUR FITRIYANI Iqbal Afriyadi Ira Nia Sanita Irsyad, As'Ary Sahlul Irzal Arief Wisky Ismail Virgo Istianingsih, Nanik Iswandi Saputra Jefdy Kurniawan Jeri Wandana Juansen, Monsya Jufri, Fikri Ramadhan Jufriadif Na`am, Jufriadif Juledi, Angga Putra Julius Santony Junadhi, Junadhi Kareem, Shahab Wahhab Khairul Azmi Kurniawan, Jefdy Kurniawan, Mhd Hary Lengga S. Sandy Leony Lidya Lidya, Leoni Lubis, Fitri Amelia Sari Lubis, Siti Sahara Lusiana Lusiana M Syahputra M. Ibnu Pati M. Iqbal Zuqron M. Syahputra Mardayatmi, Suci Mardian, Zurni Mardison Mardison Mardison Marfalino, Hari Meilinda Sari Meilinda Sari Melissa Triandini Menhard, Menhard Mhd Hary Kurniawan Miftahul Hasanah Miftahul Hasanah, Miftahul Mike Zaimy Monsya Juansen Muhammad Dahria Muhammad Tajuddin MUHAMMAD TAJUDDIN Muhammad, Abulwafa Muhammad, L. J. Mukhlis Santoso Mulyanda, Sandy Mutiana Pratiwi Nadya Alinda Rahmi Nandan Limakrisna Nanik Istianingsih Nori Sahrun Nori Sahrun, Nori Novi Yanti Nur Aini Nurcahyo, Gunadi Nurcahyo, Gunadi Widi Nurdin, Yogi K Nurhadi Nurhidayat Nursyahrina Okfalisa, - Okmarizal, Bisma Olivia, Ladyka Febby Pandu Pratama Putra, Pandu Pratama Pati, Muhammad Ibnu Pipin Refina Afindania Pratiwi, Mutiana Pulungan, Akhiruddin Purnomo, Nopi Putra, Akmal Darman Putra, Rahman Arief Putra, Ramdani Bayu Putra, Surya Dwi Putri, Adek Putri, Dhena Marichy Putri, Yozi Aulia Putut Wicaksono, Putut R Rahmiyanti Radillah, Teuku Rafika Sani Rafiska, Rian Rafki, Rafnelly Rahmad Aditiya Rahmad Rahmad Rahmadani Hidayat Rahman Arief Putra Rahmi Fauzana Rahmi, Nadya Alinda Rakhmad Pribowo Hariputra Ramadhan, Mukhlis Ramadhanu, Agung - Randy Permana Rani, Larissa Navia Refina Afindania, Pipin Resnawita, R Rezki - Rezki Rusydi Rezti Deawinda Parinduri Rian Kurniawan Rianti, Eva Rico Anggara Rini Sovia Rini Sovia Rio Andika Malik Ritna Wahyuni Rizki Mubarak Roza Marmay Roza, Yesi Betriana Ruri Hartika Zain Rusdianto Roestam Rusdianto Roestam Rustam, Camila S Sumijan S Sumijan Sabil, Muhammad Said, Abdul Azis Saiful Nurarif Sandrawira Anggraini Sani, Rafikasani Sari, Imrah Sari, Laynita Selfi Melisa Septiano, Renil Setiawan, Adil Sharon Shaza Alturky Silfia Andin Sintia Sintia Siregar, Diffri Solihin Siregar, Fajri Marindra Siswahyudianto Sitanggang, Sahat Sonang Slamet Riyadi Sofika Enggari Sovia, Rini Sri Dewi Sri Dewi Sri Dewi, Apriandini Sri Rahmawati Suci Mardayatmi Suhefi Oktarian Sukardi Sulastri Sulastri Sumijan Sumijan Sumijan Sumijan Sumijan Sumijan Sumijan Sumijan Sumijan Sumijan Sumijan Sumijan Sumijan Sumijan Sumijan Sumijan Sumijan Sumijan Sumijan Sumijan, S Surmayanti, Surmayanti Surya Dwi Putra Suryani, Vivi Susandri, Susandri Susriyanti, Susriyanti Syafri Arlis Syafrika Deni Rizki Syaljumairi, Raemon Syofneri, Nandel Tamaza, Muhammad Abyanda Teri Ade Putra Tesa Vausia Sandiva tukino, tukino Tukino, Tukino Veri, Jhon Veza, Okta Virgo, Ismail Vitriani, Vitriani Wahyu, Fungki Wanto, Anjar Wenni Afrodita Weri Sirait Y Yuhandri Yamin, Abdul Yamin Yemi, Leonardo Yenila, Firna Yerri Kurnia Febrina Yetti Fitriani Yogi K. Nurdin Yoni Aswan Yuda Irawan Yudha Aditya Fiandra Yuhandri Yuhandri Yuhandri Yuhandri Yuhandri Yuhandri Yuhandri Yuhandri Yuhandri, Yuhandri Yul Antonisfia Yulasmi Yulasmi, Yulasmi Yuli Hartati Yulihartati, Sandra Yunus, Yuhandri Yusma Elda Zakir, Supratman Zia Rahimi, Hadisha Zulharbi Zulharbi Zulvitri, Z