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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) 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 Knowledge Engineering and Data Science JURNAL MEDIA INFORMATIKA BUDIDARMA 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 Mobile and Forensics Jurnal Informatika dan Rekayasa Perangkat Lunak International Journal of Advances in Data and Information Systems Jurnal REKSA: Rekayasa Keuangan, Syariah dan Audit 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
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Capacity building for LAZIS administration through Google Applications Training Yuliansyah, Herman; Fahana, Jefree; Prahara, Adhi; Masitha, Alya
KACANEGARA Jurnal Pengabdian pada Masyarakat Vol 8, No 4 (2025): November
Publisher : Institut Teknologi Dirgantara Adisutjipto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28989/kacanegara.v8i4.3039

Abstract

The rapid development of information technology has prompted social institutions such as LazisMU to adopt digital administrative systems. However, technical limitations among staff hinder optimal implementation. This community service aimed to enhance the administrative capacity of LazisMU staff through targeted training in Google Applications. The training involved needs assessment, module development, and interactive sessions covering Google Drive, Docs, Sheets, and Forms. Results indicate significant improvement in participants’ understanding and skills, particularly in document management, financial reporting, and data handling. Pre-test and post-test comparisons, along with direct observation, showed over 60% improvement in comprehension scores. These outcomes highlight the positive impact of digital training on operational efficiency. Further training and periodic monitoring are recommended to ensure continued digital competence development.
Enhancing Clustering Accuracy Using K-Means with Seeds Optimization Mahiruna, Adiyah; Ngatimin, Ngatimin; Destriana, Rachmat; Rachmawanto, Eko Hari; Yuliansyah, Herman; Hidayat, Muhammad Taufiq
Journal of Applied Informatics and Computing Vol. 9 No. 5 (2025): October 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i5.10458

Abstract

In this study, the development of the Mean-based method proposed by Goyal and Kumar will be carried out by changing the initial cluster center determination step, which was originally based on the origin point O (0,0), to be replaced with the arithmetic mean. To assess the performance of the proposed method, it will be compared with the Global K-means method and the Mean-based K-means method. In this study, the performance of these methods will be measured using the Davies-Bouldin Index, and the significance of the proposed method will be measured using the Friedman Test. This study proposes a method of Improving K-Means Performance through Initial Center Optimization based on Second Global Average for Clustering Osteoporosis Diagnosis of lifestyle factors. Evaluation of K-Means performance through Initial Center Optimization based on Second Global Average with DBI measurements. The targeted experimental results of this study include improving the performance of K-means optimized through the initial center based on Second Global Average. From the results of nine experiments with the number of clusters [2,3,4,5,6], it can be seen that the method proposed in this study has the same superior performance compared to the Mean Based method and compared to the Global K-means method.
RANCANG BANGUN APLIKASI ANDROID POS (POINT OF SALE) KAFE UNTUK KASIR PORTABLE DAN BLUETOOTH PRINTER Pamungkas, Gilang; Yuliansyah, Herman
JST (Jurnal Sains dan Teknologi) Vol. 6 No. 1 (2017)
Publisher : Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (635.996 KB) | DOI: 10.23887/jstundiksha.v6i1.8828

Abstract

Kafe merupakan salah satu jenis usaha di bidang kuliner yang banyak diminati pengunjung. Beberapa permasalahan yang ada adalah sistem transaksi keuangan di kafe belum memanfaatkan kasir digital, hanya berupa mesin drawer. Sehingga terdapat batasan pada perhitungan transaksi. Tujuan dari penelitian menghasilkan aplikasi kasir tablet android untuk membantu proses transaksi penjualan dan dapat merekapitulasi laporan data transaksi di kafe. Selain itu, pada aplikasi ini ditambahkan fitur pencetakan kwitansi untuk pelanggan. Pengujian aplikasi android dilakukan dengan metode unit test dan menunjukkan sudah berjalan dengan lancar dan tidak ada method yang error, sehingga dapat dinyatakan lolos. Selain itu, pengujian black box test dapat disimpulkan bahwa aplikasi berjalan sesuai dengan yang telah dirancang.
Edukasi Pencegahan Demam Berdarah Dengue (Dbd) Untuk Meningkatkan Pengetahuan Jumantik Cilik Di Kelurahan Ambarketawang Handayani, Lina; Sulistyawati, Sulistyawati; Nisa Novianti, Tria; Fitriani, Isah; Jumaedi Nasir, Ardiansyah; Wahyuni Sukesi, Tri; Nafiati, Lu’lu’; Asti Mulasari, Surahma; Yuliansyah, Herman; Tentama, Fatwa
Humanism : Jurnal Pengabdian Masyarakat Vol 6 No 1 (2025): April
Publisher : Universitas Muhammadiyah Surabaya

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

Abstract

Abstrak Demam Berdarah Dengue (DBD) adalah penyakit yang sulit dideteksi karena dapat tidak menunjukkan gejala sama sekali dan juga bisa menunjukkan gejala yang sangat parah. Di Indonesia, kasus DBD terus meningkat karena mobilitas, kepadatan penduduk, dan perubahan iklim. Kabupaten Sleman, khususnya Kelurahan Ambarketawang, memiliki tingkat kasus DBD yang tinggi pengabdian kepada masyarakat ini bertujuan untuk meningkatkan pengetahuan kader jumantik cilik mengenai pencegahan DBD. Kegiatan ini dilaksanakan melalui metode edukasi kesehatan dan evaluasi. Edukasi kepada 117 kader jumantik cilik dilakukan menggunakan media video animasi dan PowerPoint. Media ini merupakan sebuah pendekatan edukasi yang interaktif dan menarik. Hal ini diharapkan dapat meningkatkan pengetahuan anak-anak tentang DBD dan memotivasi masyarakat untuk menjaga lingkungan agar bebas dari sarang nyamuk. Pengetahuan diukur menggunakan kuesioner pretest dan posttest. Hasil Uji Wilcoxon menunjukkan terdapat peningkatan pengetahuan kader jumantik cilik secara signifikan (p-value 0,000). Edukasi pencegahan DBD dengan media video animasi dan PowerPoint berhasil meningkatkan pengetahuan kader jumantik cilik. Edukasi mengenai pencegahan DBD sejak usia dini akan menciptakan kebiasaan positif yang berkelanjutan, sehingga dapat mengurangi kasus DBD di masa mendatang.
Pencegahan Stunting dengan Inovasi Teknologi berupa Modifikasi Timbangan Digital Terkoneksi Android Sudarsono, Bambang; Sukesi, Tri Wahyuni; Tentama, Fatwa; Mutmainah, Nur Fitri; Yuliansyah, Herman; Mulasari, Surahma Asti; Nafiati, Lu’lu’; Sulistyawati, Sulistyawati; Ghozali, Fanani Arief
Prima Abdika: Jurnal Pengabdian Masyarakat Vol. 3 No. 4 (2023): Volume 3 Nomor 4 Tahun 2023
Publisher : Program Studi Pendidikan Guru Sekolah Dasar Universitas Flores Ende

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37478/abdika.v3i4.3176

Abstract

Stunting is a problem that has a negative impact on society and can last for a long time. This community service program aims to increase the knowledge and skills of partner communities in preventing stunting by using innovative technology called the "Tas Stunting Kit." This activity involves creating appropriate technology, training health cadres, and evaluating technological innovation. The digital scale is modified to connect to an Android tablet via Bluetooth and is packaged in a bag. This community service program was attended by 29 participants, all of whom were health cadres, with the implementing team consisting of the chief proposer, additional proposers, and a second team consisting of lecturers and students. This technological innovation was evaluated with the Software Usability Scale (SUS) as a series of modifications to digital scales and Android applications, with the results having an acceptable acceptability range. The subsequent follow-up is developing a more integrated data collection system as a basis for the village government to make data-based decisions.
Aspect-Based Sentiment Analysis of User Reviews on the Game “Honkai: Star Rail” Using Naïve Bayes Classifier Agus Setiawan, Hisyam; Yuliansyah, Herman
Sistemasi: Jurnal Sistem Informasi Vol 13, No 5 (2024): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v13i5.4343

Abstract

Game is a form of entertainment that is often used to refresh the mind from the fatigue of daily activities and routines. Honkai: Star Rail is a popular turn-based game from Hoyoverse available on Google Play Store. Several studies have proposed Sentiment Analysis with Naïve Bayes classification method. However, not many have identified the reviews of a game to the extent of identifying on its aspects. In aspect-based sentiment analysis, text is analyzed to identify various attributes or components, then the relevant sentiment (positive, negative, or neutral) for each of these attributes is determined. This research aims to analyze aspect-based sentiment using the Naïve Bayes Classifier method, as well as categorize sentiment into positive and negative, and classify reviews into certain aspects. The results obtained after 5-fold iteration obtained the best average accuracy of 79%, The evaluation results show that it is necessary to tune the model using Grid Search Hyperparameter Tuning. Optimization of smoothing parameters with alpha = 0.1 proved effective in improving model performance with the highest weighted average accuracy of 93%. The evaluation results show that Grid Search Hyperparameter Tuning optimization gives better performance to the Naive Bayes algorithm model in multi-label classification.
PREDICTING LOAN ELIGIBILITY WITH SUPPORT VECTOR MACHINE: A MACHINE LEARNING APPROACH Rajunaidi, Rajunaidi; Yuliansyah, Herman; Sunardi, Sunardi; Murinto, Murinto
JURTEKSI (jurnal Teknologi dan Sistem Informasi) Vol. 11 No. 3 (2025): Juni 2025
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) STMIK Royal Kisaran

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33330/jurteksi.v11i3.3876

Abstract

Abstract: Non-performing loans remain one of the main challenges faced by cooperatives, particularly when the loan eligibility assessment process is still conducted manually. This traditional approach tends to be time consuming, subjective, and prone to inaccurate decisions. This study aims to develop a predictive model for borrower eligibility using the Support Vector Machine (SVM) algorithm as a more efficient and objective machine learning-based solution. A total of 1,000 loan history records were processed using RapidMiner software, taking into account variables such as salary, years of employment, loan amount, monthly installment, employment status, monthly expenses, number of dependents, housing status, age, and collateral value. The model’s performance was evaluated using a confusion matrix and classification metrics including accuracy, precision, recall, and kappa. The results indicate that the SVM model achieved an accuracy of 90.05%, precision of 90.13%, recall of 90.05%, and f1 score of 90,08%, reflecting a strong performance in classifying borrower eligibility. The application of this method makes a significant contribution to the development of data driven decision support systems within cooperative environments. This finding expands the scientific understanding in the field of microfinance and supports the implementation of artificial intelligence technologies in making decisions that are more precise, rapid, and accurate.Keywords: cooperative; eligibility prediction; machine learning; non-performing loan; SVMAbstrak: Kredit macet merupakan salah satu permasalahan utama yang dihadapi koperasi, terutama ketika proses penilaian kelayakan peminjam masih dilakukan secara manual. Pendekatan ini cenderung lambat, subjektif, dan berisiko menghasilkan keputusan yang kurang akurat. Penelitian ini bertujuan untuk membangun model prediksi kelayakan peminjam menggunakan algoritma Support Vector Machine (SVM) sebagai solusi berbasis machine learning yang lebih efisien dan objektif. Sebanyak 1.000 data riwayat pinjaman diolah menggunakan tools RapidMiner dengan mempertimbangkan variabel: gaji, lama bekerja, besar pinjaman, angsuran per bulan, status pegawai, pengeluaran bulanan, jumlah tanggungan, status rumah, umur, dan nilai jaminan. Evaluasi model dilakukan menggunakan confusion matrix dan metrik klasifikasi seperti akurasi, presisi, recall, dan kappa. Hasil menunjukkan bahwa model SVM mencapai akurasi  90,05%, presisi 90,13%, recall 90,05%, dan f1 score 90,08%, yang mencerminkan performa model yang sangat baik dalam mengklasifikasikan kelayakan peminjam. Penerapan metode ini memberikan kontribusi penting dalam pengembangan sistem pendukung keputusan berbasis data di lingkungan koperasi. Temuan ini memperluas wawasan keilmuan di bidang keuangan mikro dan mendukung penerapan teknologi kecerdasan buatan dalam pengambilan keputusan yang lebih tepat, cepat, dan akurat.Kata Kunci: koperasi; kredit macet; machine learning; prediksi kelayakan; SVM  
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.
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 Eko Hari Rachmawanto Faisal Dharma Adhinata Fatwa Tentama Febiyan, Rifal Fitriani Mutmainah, Nur Fitriani, Isah Ghozali, Fanani Arief Habie, Khairul Fathan Hafin, Aqid Fahri Hazar, Siti Hidayat, Muhammad Taufiq Hildayanti, Ica Kurnia Hildayanti, Ica Kurnia Hussien, Nur Syahela Ika Arfiani Imam Riadi Jayawarsa, A.A. Ketut Jefree Fahana Jumaedi Nasir, Ardiansyah Khoirul Anam Dahlan Khoirunnisa, Itsnaini Irvina Kintung Prayitno, Kintung Lestari, Agung Tri Lifa, Lifa Lina Handayani Listyaningrum, Prabandari M. Yogi Riyantama Isjoni Mahiruna, Adiyah 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 Nisa Novianti, Tria Novitasari, Isda Desy Nur Rochmah Dyah Pujiastuti Pamungkas, Gilang Pamungkas, Gilang Pratama, Ridho Haikal Pratama, Wegig Putro, Aldibangun Pidekso Quix, Christoph R. Hafid Hardyanto, Settings Rachmaliany, Nur Rahmawati, Rahmawati Raihan, Habib Aulia Rajunaidi, Rajunaidi Razak, Farhan Radhiansyah Rusydi Umar Salji, Rinday Zildjiani Sri Winiarti Subardjo Subardjo, Subardjo Sukesi , Tri Wahyuni Sulistyawati , Sulistyawati Sulistyawati Sulistyawati Sunardi Sunardi Sunardi Surahma Asti Mulasari Tri Wahyuni Sukesi Ulumiyah, Iftitah Dwi Wahyuni Sukesi, Tri Wala, Jihan Wan Ali, Wan Nur Syamilah Wibowo, Merlinda Yohanni Syahra Yulianto, Dinan Yulisasih, Baiq Nikum