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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Jurnal Pendidikan Vokasi Jurnal Sains dan Teknologi Jurnal Sarjana Teknik Informatika Jurnal Teknologi Informasi dan Ilmu Komputer Sistemasi: Jurnal Sistem Informasi Jurnal Pengabdian UntukMu NegeRI Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer Sinkron : Jurnal dan Penelitian Teknik Informatika JURNAL MEDIA INFORMATIKA BUDIDARMA Jurnal Penelitian Pendidikan IPA (JPPIPA) Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control JITK (Jurnal Ilmu Pengetahuan dan Komputer) JOURNAL OF APPLIED INFORMATICS AND COMPUTING ILKOM Jurnal Ilmiah Compiler KACANEGARA Jurnal Pengabdian pada Masyarakat Martabe : Jurnal Pengabdian Kepada Masyarakat JURTEKSI Jurnal Pemberdayaan: Publikasi Hasil Pengabdian Kepada Masyarakat Jambura Journal of Informatics Building of Informatics, Technology and Science JISKa (Jurnal Informatika Sunan Kalijaga) Mobile and Forensics Aviation Electronics, Information Technology, Telecommunications, Electricals, Controls (AVITEC) Jurnal Informatika dan Rekayasa Perangkat Lunak International Journal of Advances in Data and Information Systems Jurnal REKSA: Rekayasa Keuangan, Syariah dan Audit Innovation in Research of Informatics (INNOVATICS) Humanism : Jurnal Pengabdian Masyarakat Jurnal Pendidikan dan Teknologi Indonesia Prima Abdika: Jurnal Pengabdian Masyarakat Bulletin of Pedagogical Research Jurnal Pengabdian Pada Masyarakat Jurnal Pengabdian Informatika (JUPITA) Bulletin of Social Informatics Theory and Application Sabangka Abdimas Jurnal Pengabdian Masyarakat Sabangka Mohuyula : Jurnal Pengabdian Kepada Masyarakat Scientific Journal of Informatics
Herman Yuliansyah, Herman
Program Studi Teknik Informatika, Fakultas Teknologi Industri, Universitas Ahmad Dahlan
Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Astronomy Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Neuroscience Nursing Physics Public Health Social Sciences Transportation Veterinary Other

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Development of Integrated Project-based (PjBL-T) model to improve work readiness of vocational high school students Sudarsono, Bambang; Tentama, Fatwa; Mulasari, Surahma Asti; Sukesi, Tri Wahyuni; Sulistyawati, Sulistyawati; Ghozali, Fanani Arief; Yuliansyah, Herman; Nafiati, Lu'lu'; Sofyan, Herminarto
Jurnal Pendidikan Vokasi Vol. 12 No. 3 (2022): November
Publisher : ADGVI & Graduate School of Universitas Negeri Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21831/jpv.v12i3.53158

Abstract

The unemployed from vocational high schools (SMK) is still high. Several factors, including the low job readiness of vocational students and the small availability of jobs, cause the high unemployment rate for vocational schools. This study aims to develop a learning model of Integrated Project Based Learning (PjBL-T) and test the effectiveness of the PjBL-T model in preparing vocational students' job readiness. The design of this study adopted the Richey and Klein model stages. This research was carried out in three stages: model development, internal validation, and external validation. This study used research subjects consisting of 10 vocational teachers, ten industrial practitioners, and 54 Automotive Engineering SMK Muhammadiyah 2 Tempel students. The research objects used were SMK Muhammadiyah 2 Tempel, Jogjakarta Automotive Center (OJC) Auto Service, Barokah Auto Service, Astra Daihatsu Armada, and Gadjah Mada Auto Service. The data collection techniques used were focus group discussions (FGD), questionnaires, and practice assessment sheets. Data were analyzed descriptively. The PjBL-T model is feasible and can be applied according to the learning objectives. The effectiveness of increasing student work readiness tested limited and expanded and improved very well with a score of 3.27.
DESA MANDIRI SEHAT BEBAS STUNTING BERBASIS EDUKASI DAN INOVASI DI TEGALREJO GUNUNGKIDUL Tentama, Fatwa; wahyuni Sukesi, Tri; Fitriani Mutmainah, Nur; Sudarsono, Bambang; Asti Mulasari, Surahma; Nafiati, Lu'lu'; Sulistyawati, Sulistyawati; Yuliansyah, Herman; Arief Ghozali, Fanani
Martabe : Jurnal Pengabdian Kepada Masyarakat Vol 7, No 7 (2024): MARTABE : JURNAL PENGABDIAN MASYARAKAT
Publisher : Universitas Muhammadiyah Tapanuli Selatan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31604/jpm.v7i7.2784-2793

Abstract

Kejadian stunting merupakan permasalahan utama mitra yaitu Kalurahan Tegalrejo, Gedangsari yang merupakan lokus stunting tertinggi di Kabupaten Gunungkidul. Stunting merupakan permasalahan nasional yang upaya pencegahannya harus melibatkan lintas sektor agar terwujud kemandirian kesehatan dalam pencegahan stunting. Perlu dilakukan kerjasama antara masyarakat, pemerintah dan perguruan tinggi agar permasalah stunting ini dapat dikendalikan. Tujuan kegiatan ini adalah untuk meningkatkan pengetahuan dan keterampilan masyarakat mitra dalam melakukan pencegahan terhadap kejadian stunting dengan menggunakan inovasi teknologi sebagai pendukung. Pada akhirnya muara dari kegiatan ini adalah terwujudnya kemandirian kesehatan dalam upaya pencegahan stunting. Metode pelaksanaan program dilakukan dengan penyuluhan, pelatihan, dan praktik termasuk pelatihan dan praktik penggunaan alat-alat dalam pencegahan stunting. Pelatihan ini dilaksanakan selama delapan hari. Sasaran utama pada pelatihan ini adalah kader kesehatan dan perangkat desa di Tegalrejo, Gunungkidul. Hasil kegiatan ini masyarakat mitra memperoleh pengetahuan dan ketrampilan mengenai pelatihan pencegahan stunting, pelatihan penyusunan RPJMDES yang mendukung dalam upaya pencegahan stunting, pelatihan penggunaan alat tas stunting dan timbangan stunting untuk balita, pelatihan penggunaan insinerator skala rumah tangga dan adanya penguatan pengurus posyandu remaja. Selain itu masyarakat mitra juga mendapatkan peralatan pencegahan stunting yang dapat dimanfaatkan oleh kader-kader kesehatan. Program ini dapat diterima dengan antusias oleh masyarakat mitra dan mendapat apresiasi dan dukungan oleh kalurahan Tegalrejo secara maksimal.
Model Klasifikasi Emosi Berbasis Teks dengan Algoritma Decision Tree dan Support Vector Machine Raihan, Habib Aulia; Yuliansyah, Herman; Murinto
Jurnal Informatika dan Rekayasa Perangkat Lunak Vol. 7 No. 2 (2025): September
Publisher : Universitas Wahid Hasyim

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Text-based communication has become a key means of interaction across various sectors. Previous studies have applied supervised learning algorithms to emotion classification in text. These studies used different datasets, but this diversity also introduced a risk of overfitting in text-based emotion classification models. Consequently, the use of cross-validation and hyperparameter optimization is required to ensure the model’s generalization ability. The aim of this research is to compare the performance of two supervised learning algorithms—Decision Tree (DT) and Support Vector Machine (SVM)—for emotion classification on an English-language text dataset of 16,000 labeled entries (anger, fear, joy, love, sadness, surprise) sourced from Kaggle. The dataset undergoes cleaning, tokenization, stopword removal, and lemmatization, after which features are extracted using TF-IDF. Both algorithms are evaluated with K-Fold and Stratified K-Fold cross-validation, then used to compute metrics of accuracy, precision, recall, and F1-score. Classification results show that the hyperparameter-tuned DT achieved an average accuracy of 88%, while the hyperparameter-tuned SVM achieved 89%. Meanwhile, Stratified K-Fold cross-validation yielded an accuracy variance of just 0.02% for DT and 0.15% for SVM. Therefore, it can be concluded that Stratified K-Fold performs better than standard K-Fold on imbalanced datasets, and that hyperparameter-tuned SVM outperforms hyperparameter-tuned DT.
PREDICTIVE MODEL FOR COOPERATIVE LOAN RECIPIENT ELIGIBILITY USING SUPERVISED MACHINE LEARNING Rajunaidi, Rajunaidi; Yuliansyah, Herman; Sunardi, Sunardi; Murinto, Murinto
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 11 No. 2 (2025): JITK Issue November 2025
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v11i2.7235

Abstract

Non-performing loans remain a critical challenge for cooperatives as they can undermine financial stability, erode member trust, and impede institutional growth. This study develops a predictive model for cooperative loan eligibility using supervised machine learning techniques and a novel three-class classification framework, Approved, Consideration, and Rejected, to support more objective and transparent decision-making. A dataset of 1,000 borrower records containing demographic and financial attributes was analyzed using Naive Bayes, Decision Tree, and Random Forest algorithms implemented in RapidMiner. The Random Forest algorithm achieved the best predictive performance with an accuracy of 96.02%, demonstrating its robustness and reliability compared to the other models. The proposed three-class system differentiates this study from conventional binary classification approaches, enabling finer distinctions among borrower categories and promoting fairness in cooperative credit evaluations. The findings provide practical guidance for cooperatives to adopt data-driven, transparent, and accountable decision-making systems that reduce manual bias and strengthen financial inclusion. Overall, the proposed three-class model built through a supervised learning framework offers a reliable, fair, and scalable solution to support sustainable lending practices and enhance risk management in cooperative institutions.
Pelatihan Kreasi Konten Digital dengan Komunikasi melalui Tools Kecerdasan Artifisial Winiarti, Sri; Soyusiawaty, Dewi; Umar, Rusydi; Yuliansyah, Herman
Jurnal Pengabdian UntukMu NegeRI Vol. 9 No. 3 (2025): Pengabdian Untuk Mu negeRI
Publisher : LPPM UMRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/jpumri.v9i3.10417

Abstract

Seiring dengan adanya kebijakan Kementrian Pendidikan Dasar dan Menengah Republik Indonesia terkait penerapan Kecerdasan Artifisial (KA) dalam pembelajaran jenjang Sekolah Dasar (SD) hingga Sekolah Menengah Atas (SMA), maka semua sekolah memerlukan adanya pemahaman terhadap pelaksanaan pembelajaran KA. Perkembangan KA telah memberikan peluang baru dalam mendukung kreativitas dan komunikasi digital dalam pelaksanaan pembelajaran. Namun, banyak guru masih kesulitan berinteraksi secara efektif dengan perangkat KA untuk menghasilkan konten pembelajaran yang meraik, khususnya untuk pembelajaran dengan model unplugged. Kegiatan pengabdian kepada masyarakat ini bertujuan meningkatkan kompetensi guru dalam berkomunikasi dengan perangkat KA melalui pelatihan bertema “Kreasi Konten Digital dengan Komunikasi melalui Tools Kecerdasan Artifisial.” Pelatihan dilaksanakan pada 9 Juli 2025 di Kabupaten Sleman dengan peserta sebanyak 24 guru Sekolah Menengah Pertama (SMP). Metode yang digunakan meliputi tranfer pengetahuan, praktik langsung Aplikasi KA, praktek mengajar dengan Aplikasi KA dengan pendekatan PjBL dan evaluasi pelaksanaan. Materi pelatihan berupa konsep KA dalam pembelajaran, cara berkomunikasi dengan perangkat KA yang efektif dengan menggunakan prompt yang efektif. Kegiatan pelatihan ini meningkatkan keterampilan komunikasi guru sebesar 17,4% (dari 69,4% menjadi 86,8%), menunjukkan efektivitas pendekatan PjBL dalam memperkuat literasi digital dan kreativitas guru.
Multi-Label Opinion Mining Based on Random Forest with SMOTE and ADASYN Ardiansyah, Ricy; Yuliansyah, Herman; Yudhana, Anton
Compiler Vol 14, No 2 (2025): November
Publisher : Institut Teknologi Dirgantara Adisutjipto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28989/compiler.v14i2.3185

Abstract

Multi-label classification is essential to categorize data into multiple labels simultaneously. However, data imbalance poses a challenge, where some labels have much less representation, thus reducing the model performance. This study aims to propose a candidate-based sentiment analysis model on the 2024 Jakarta Presidential and Gubernatorial Election review. The SMOTE and ADASYN oversampling methods are applied to handle class imbalance. Both oversampling methods are compared with the Random Forest machine learning method. The experimental results show that. The experimental results show that in the classification of Presidential candidates, Random Forest achieves an accuracy of 0.947 with SMOTE and 0.948 with ADASYN. For sentiment labels, the accuracy of Random Forest remains high with a result of 0.989 for both SMOTE and ADASYN. In the classification of Jakarta Gubernatorial candidates, Random Forest + SMOTE produces an accuracy of 0.975, while with ADASYN it decreases slightly to 0.973. For sentiment labels, both SMOTE and ADASYN have the highest accuracy of 0.993. The application of SMOTE and ADASYN helps to improve the distribution of the minority class without decreasing the overall accuracy, as well as improving the stability in recognizing various multi-label classes in a balanced manner.
Enhancing Medical Data Security Through Blockchain Smart Contract and Decentralized Application Herman, Herman; Salji, Rinday Zildjiani; Yuliansyah, Herman
International Journal of Advances in Data and Information Systems Vol. 6 No. 3 (2025): December 2025 - International Journal of Advances in Data and Information Syste
Publisher : Indonesian Scientific Journal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59395/ijadis.v6i3.1387

Abstract

This research studies implementation of decentralized applications (DApps) that are combined with blockchain technology and IPFS for storing patient medical data. The goal of this research is to increase the security, transparency, and access control of stored medical data to make sure only legitimate users can access the data. The proposed system uses smart contracts on the Ethereum network to handle user rights of access (doctors, patients, and admins) and ensure data integrity through the blockchain immutability feature. Patient medical records are retained in IPFS and traced using the Content Identifier (CID). Implementation outcome reveals that the system can safely process medical information, keeping patients in full control of their information, and restricting data access only to scheduled time. This system also shows the potential of blockchain and IPFS technology-based applications in achieving a more efficient health ecosystem focused on safeguarding people's data.
Prediction of Dengue Fever Cases Using the Linear Regression Method Based on Open Data from West Java Province Firdaus, Muhammad Khysam; Yuliansyah, Herman
Innovation in Research of Informatics (Innovatics) Vol 7, No 2 (2025): September 2025
Publisher : Department of Informatics, Siliwangi University, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37058/innovatics.v7i2.16143

Abstract

Dengue Hemorrhagic Fever (DHF) is a widespread disease in tropical regions, including Indonesia. West Java Province reports the highest number of cases, influenced by factors such as rainfall, population density, and total population. Accurate prediction of DHF cases is essential for effective prevention and control strategies. This study aims to propose a predictive model for DHF cases in West Java using the Linear Regression method and to evaluate its performance using Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE) metrics. The research utilizes secondary data from 2014 to 2023 on DHF cases, population density, and total population from the Open Data Jabar platform. Rainfall data were collected from Badan Meteorologi, Klimatologi, dan Geofisika (BMKG) and Badan Pusat Statistik Indonesia (BPS). The research process includes data collection, preprocessing, time series splitting, model training and iteration, prediction, and performance evaluation. The results show that among the five focus regions, Bandung City achieved the best prediction performance, with a MAPE of 45.82% and an RMSE of 1216.105. These findings indicate that Multiple Linear Regression is reasonably effective for predicting DHF cases, particularly in Bandung. Despite limitations in data availability—especially rainfall data—the model provides informative insights. Future work could improve prediction accuracy by incorporating additional independent variables and more advanced modeling techniques, such as machine learning.
Klasifikasi Tingkat Serangan pada Log Jaringan Siber dengan Komparasi Naive Bayes dan K-Nearest Neighbor Apriliani, Evinda; Winiarti, Sri; Riadi, Imam; Yuliansyah, Herman
Building of Informatics, Technology and Science (BITS) Vol 7 No 3 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i3.8765

Abstract

The increasing threat of cybersecurity poses a significant impact on both organizations and individuals, necessitating a system capable of accurately detecting and classifying attack levels to support prioritization of responses. This study aims to analyze and compare the performance of two machine learning algorithms, Naive Bayes and K-Nearest Neighbor (KNN), in classifying cyberattack levels, and to evaluate the effect of hyperparameter tuning on improving model accuracy. The research methods included utilizing the cybersecurity_attacks dataset, data preprocessing, model training at three data split ratios (70:30, 80:20, and 90:10), and parameter optimization using Randomized Search and Grid Search. Performance evaluation was based on accuracy, precision, recall, and F1-score values. The results showed that KNN performed best, with a peak accuracy of 0.96 at the 80:20 ratio after tuning, increased from an accuracy of 0.947 before tuning, with precision, recall, and F1-score values ​​ranging from 0.95 to 0.96. Meanwhile, Naive Bayes only achieved a peak accuracy of 0.8485 at the same ratio. Although the improvement after hyperparameter tuning was not significant, this process still resulted in a more stable and consistent model. Future research is recommended to explore ensemble methods and test them on other datasets to produce more adaptive cyberattack classification models.
Perbandingan Kinerja MobileNetV2 dan VGG16 dalam Klasifikasi Penyakit pada Citra Daun Tanaman Cabai Khoirunnisa, Itsnaini Irvina; Fadlil, Abdul; Yuliansyah, Herman
JISKA (Jurnal Informatika Sunan Kalijaga) Vol. 11 No. 1 (2026): January 2026
Publisher : UIN Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/jiska.5075

Abstract

Chili peppers play a crucial role in the Indonesian economy, serving as a significant source of income for many farmers. Price fluctuations influenced by weather conditions make this crop vulnerable to diseases that can impact productivity. However, leaves are key indicators of plant health, revealing early disease symptoms before they spread. This research focuses on detecting diseases in chili plants using neural network architectures via transfer learning, specifically MobileNetV2 and VGG16, to classify chili leaf images. The study aims to identify three disease classes: begomovirus, leaf spots, and healthy leaves. The dataset comprises 3,150 leaf images, split into 70% for training and 30% for testing. Results show that MobileNetV2 achieved an accuracy of 99.47% and VGG16 98.62%, with evaluation using a confusion matrix indicating good performance in disease identification, where MobileNetV2 offers better computational efficiency. Thus, transfer learning can effectively identify leaf diseases in chili plants.
Co-Authors Abdul Fadlil Adhi Prahara, Adhi Agus Setiawan, Hisyam ALYA MASITHA Anton Yudhana Apriliani, Evinda Ardiansyah, Ricy Arief Ghozali, Fanani Asti Mulasari, Surahma Ayu Laksmi Pandhita, Ayu Laksmi Bambang Sudarsono Bella Okta Sari Miranda Bidinnika, Muhammad Kunta Darmanto Darmanto Destriana, Rachmat Dewi Soyusiawaty Dewi, Ayu Intansari Donna Setiawati Eko Hari Rachmawanto Fatwa Tentama Febiyan, Rifal Firdaus, Muhammad Khysam Fitriani Mutmainah, Nur Fitriani, Isah Ghozali, Fanani Arief Habie, Khairul Fathan Hafin, Aqid Fahri Hazar, Siti Herman Herminarto Sofyan Hidayat, Muhammad Taufiq Hildayanti, Ica Kurnia Hildayanti, Ica Kurnia Ika Arfiani Imam Riadi Irfan, Syahid Al Jayawarsa, A.A. Ketut Jefree Fahana Jumaedi Nasir, Ardiansyah Khoirul Anam Dahlan Khoirunnisa, Itsnaini Irvina Kintung Prayitno, Kintung Lifa, Lifa Lina Handayani Listyaningrum, Prabandari Mahiruna, Adiyah Muhammad Abdul Aziz Muhammad Dzikrullah Suratin, Muhammad Dzikrullah Muhammad Fahmi Mubarok Nahdli Muhammad Kunta Biddinika Muhammad Ridwan Murinto Murinto Murinto Mutmainah, Nur Fitri Nafiati, Lu'lu' Nafiati, Lu’lu’ NGATIMIN, NGATIMIN Nia Ekawati, Nia Nisa Novianti, Tria Novitasari, Isda Desy Nur Rochmah Dyah Pujiastuti Pamungkas, Gilang Pamungkas, Gilang Pratama, Ridho Haikal Pratama, Wegig Putro, Aldibangun Pidekso R. Hafid Hardyanto, Settings Rachmaliany, Nur Rahmawan, Jihad Rahmawati, Rahmawati Raihan, Habib Aulia Rajunaidi, Rajunaidi Razak, Farhan Radhiansyah Rohmadi, Yusuf Eko Rusydi Umar Salji, Rinday Zildjiani Sri Winiarti Subardjo Subardjo, Subardjo Sukesi , Tri Wahyuni Sulistyawati , Sulistyawati Sulistyawati Sulistyawati Sunardi Sunardi Sunardi Surahma Asti Mulasari Tole Sutikno Tri Wahyuni Sukesi Ulumiyah, Iftitah Dwi Wahyuni Sukesi, Tri Wala, Jihan Wan Ali, Wan Nur Syamilah Yohanni Syahra Yulianto, Dinan Yulisasih, Baiq Nikum