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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Jurnal Sains dan Teknologi Jurnal Teknologi Proceedings Konferensi Nasional Sistem dan Informatika (KNS&I) JOIV : International Journal on Informatics Visualization Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Creative Information Technology Journal Jurnal Eksplora Informatika JITK (Jurnal Ilmu Pengetahuan dan Komputer) Jurnal Pengabdian Masyarakat AbdiMas JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Jurnal FATEKSA : Jurnal Teknologi dan Rekayasa JTIULM (Jurnal Teknologi Informasi Universitas Lambung Mangkurat) Journal of Electrical Engineering and Computer (JEECOM) Journal of Applied Data Sciences KLIK: Kajian Ilmiah Informatika dan Komputer Bridge: Jurnal Publikasi Sistem Informasi dan Telekomunikasi Jurnal ilmiah teknologi informasi Asia Jurnal Sistem Informasi Komputer dan Teknologi Informasi
TONNY HIDAYAT
Universitas AMIKOM Yogyakarta
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Mechanical Engineering

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Analisis Clustering Pegawai Berdasarkan Tingkat Kedisiplinan Menggunakan Algoritma K-Means dan Davies-Bouldin Index Alfian, Wahyu; -, Kusrini; Hidayat, Tonny
Journal of Electrical Engineering and Computer (JEECOM) Vol 6, No 2 (2024)
Publisher : Universitas Nurul Jadid

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33650/jeecom.v6i2.9556

Abstract

Fenomena kedisiplinan pegawai dalam organisasi menjadi salah satu aspek penting yang mempengaruhi efisiensi dan efektivitas operasional. Dalam konteks rumah sakit kedisiplinan pegawai tidak hanya berdampak pada kelancaran operasional tetapi juga berhubungan langsung dengan kualitas pelayanan. Namun, pengukuran dan penentuan tingkat kedisiplinan pegawai seringkali menjadi tantangan tersendiri. Metode tradisional seperti penilaian manual cenderung subjektif dan tidak konsisten. Oleh karena itu, diperlukan metode yang lebih objektif dan terstruktur untuk mengelompokkan pegawai berdasarkan tingkat kedisiplinan mereka. Data yang digunakan mencakup berbagai aspek seperti kepribadian, keterampilan teknis, kemampuan menyelesaikan tugas, dan hubungan kerja, yang dikumpulkan melalui aplikasi SIPEKA. Algoritma K-Means diterapkan untuk mengelompokkan pegawai ke dalam empat cluster, yaitu: dari 4788 data pegawai dari januari 2024 sampai juli didapatkan 1995 di dalam Cluster 1 yang berstatus sangat baik, 1936 di dalam Cluster 2 yang berstatus baik, 842 dalam Cluster 3 yang berstatus Cukup baik dan 15 dalam Cluster 4 yang berstatus kurang baik. Evaluasi Cluster dilakukan dengan menggunakan Davies-Bouldin Index (DBI) untuk mengukur validitas dan kepaduan cluster yang terbentuk. Hasil penelitian menunjukkan bahwa penentuan jumlah cluster (k=4) dan titik pusat (centroid) awal sangat berpengaruh terhadap hasil akhir Clusterisasi. Nilai DBI yang diperoleh sebesar 1.89 mengindikasikan bahwa nilai tersebut menandakan bahwa ada beberapa tingkat overlap atau ketidaksempurnaan dalam pemisahan cluster, meskipun nilai ini tidak terlalu buruk. Namun, tidak bisa disebut hasil clustering yang optimal, karena nilai yang ideal seharusnya mendekati 0.Fenomena kedisiplinan pegawai dalam organisasi menjadi salah satu aspek penting yang mempengaruhi efisiensi dan efektivitas operasional. Dalam konteks rumah sakit kedisiplinan pegawai tidak hanya berdampak pada kelancaran operasional tetapi juga berhubungan langsung dengan kualitas pelayanan. Namun, pengukuran dan penentuan tingkat kedisiplinan pegawai seringkali menjadi tantangan tersendiri. Metode tradisional seperti penilaian manual cenderung subjektif dan tidak konsisten. Oleh karena itu, diperlukan metode yang lebih objektif dan terstruktur untuk mengelompokkan pegawai berdasarkan tingkat kedisiplinan mereka. Data yang digunakan mencakup berbagai aspek seperti kepribadian, keterampilan teknis, kemampuan menyelesaikan tugas, dan hubungan kerja, yang dikumpulkan melalui aplikasi SIPEKA. Algoritma K-Means diterapkan untuk mengelompokkan pegawai ke dalam empat cluster, yaitu: dari 4788 data pegawai dari januari 2024 sampai juli didapatkan 1995 di dalam Cluster 1 yang berstatus sangat baik, 1936 di dalam Cluster 2 yang berstatus baik, 842 dalam Cluster 3 yang berstatus Cukup baik dan 15 dalam Cluster 4 yang berstatus kurang baik. Evaluasi Cluster dilakukan dengan menggunakan Davies-Bouldin Index (DBI) untuk mengukur validitas dan kepaduan cluster yang terbentuk. Hasil penelitian menunjukkan bahwa penentuan jumlah cluster (k=4) dan titik pusat (centroid) awal sangat berpengaruh terhadap hasil akhir Clusterisasi. Nilai DBI yang diperoleh sebesar 1.89 mengindikasikan bahwa nilai tersebut menandakan bahwa ada beberapa tingkat overlap atau ketidaksempurnaan dalam pemisahan cluster, meskipun nilai ini tidak terlalu buruk. Namun, tidak bisa disebut hasil clustering yang optimal, karena nilai yang ideal seharusnya mendekati 0.
User Interface Yang Adaptif Pada Kernwerk Mobile App Berbasis Ekstensi Modular UEQ+ Alif Syaiful Huda; Alva Hendi Muhammad; Tonny Hidayat
Bridge : Jurnal Publikasi Sistem Informasi dan Telekomunikasi Vol. 2 No. 2 (2024): Mei : Bridge : Jurnal Publikasi Sistem Informasi dan Telekomunikasi
Publisher : Asosiasi Profesi Telekomunikasi Dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/bridge.v2i2.44

Abstract

The diversity in societal exercise preferences has increased significantly, with fitness emerging as a favored modern activity, particularly in urban areas of Indonesia. Fitness is valued for its effectiveness in restoring body fitness and achieving ideal body shapes swiftly. However, in the era of Industry 4.0, technological advancements have revolutionized the approach to fitness. Smartphone fitness applications have replaced the role of personal trainers by providing tailored exercise and dietary programs. User Interface (UI) plays a pivotal role in fitness applications, influencing User Experience (UX). The challenge lies in designing UI to accommodate user heterogeneity, both internally and externally. Adaptive UI emerges as a solution, capable of altering layout and content according to user characteristics. Kernwerk® Functional Fitness exemplifies a fitness application utilizing AI to optimize fitness routines. To enhance Kernwerk's UI adaptability, UX evaluation is conducted using UEQ+ modular extension, a comprehensive instrument for effectively and efficiently measuring user experience. Through this evaluation, components of UI and UX requiring further development to enhance Kernwerk's adaptability can be identified.
Grouping of Image Patterns Using Inceptionv3 For Face Shape Classification Hidayat, Tonny; Astuti, Ika Asti; Yaqin, Ainul; Tjilen, Alexander Phuk; Arifianto, Teguh
JOIV : International Journal on Informatics Visualization Vol 7, No 4 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.7.4.1743

Abstract

The human face is an extraordinary part where nearly everybody is not quite the same as each other. One perspective that should be visible plainly is the shape. Face shape grouping can be used for amusement, security, or excellence. One technique that can be utilized in picture grouping is the InceptionV3 model. InceptionV3 is the structure of the Convolutional Neural Network (CNN) created by Google, which can tackle picture examination and item discovery issues. This engineering is utilized to order face shapes into five classes: Round, Heart, Square, Oblong, and Oval. At that point, the Google Pictures dataset goes through the pre-handling stage, and the Shrewd Edge Identifier is applied to each picture. Hair turns into a commotion. Consider recognizing the side of the face because it does not make any difference what the hairdo resembles. What is important is the side of the face. When there is a dataset of elongated class and heart class with a comparable hairdo, InceptionV3 will identify the component and expect the two pieces of information to come from a similar class. The exchange learning strategy is done in preparation for the last Layer of ImageNet's InceptionV3 model. This strategy puts the high precision level with an exactness of 93% preparation and testing between 88% - 98%. InceptionV3 could arrange upwards of 692 from 747 datasets or around 92.65%. The most reduced information class is the heart class, where out of 150 information, InceptionV3 can characterize upwards of 130 information.
Classification of Mental Disorders Using Modified Balanced Random Forest And Feature Selection Arsad; Alva Hendi Muhammad; Tonny Hidayat
Jurnal Teknologi Informasi Universitas Lambung Mangkurat (JTIULM) Vol. 9 No. 2 (2024)
Publisher : Fakultas Teknik Universitas Lambung Mangkurat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20527/jtiulm.v9i2.320

Abstract

This study employs the Modified Balanced Random Forest (MBRF) algorithm and Correlation-based Feature Selector (CfsSubsetEval) for mental disorder classification. The "Mental Disorder Classification" dataset from Kaggle was used with the aim of improving accuracy, evaluating feature selection, and assessing MBRF's performance in handling data imbalance. The study compares the performance of Random Forest (RF) and MBRF, and examines the impact of feature selection using CFS on mental disorder classification. The results indicate that MBRF outperforms RF with an 8.33% improvement in accuracy, 8.61% in precision, 8.33% in recall, and 9.08% in F1-Score. Additionally, the comparison between MBRF and MBRF with CFS reveals that while accuracy and recall remain the same, MBRF achieves 0.23% higher precision and 0.81% higher F1-Score than MBRF with CFS. In conclusion, the use of MBRF proves to be superior to the standard RF in addressing data imbalance for mental disorder classification, significantly improving accuracy, precision, recall, and F1-Score. However, feature selection with CFS does not significantly enhance performance. While accuracy and recall remain unchanged, MBRF without CFS demonstrates higher precision and F1-Score, indicating that the model performs better without feature selection in maintaining the balance between precision and recall.
IMPROVING RESNET-50 PERFORMANCE FOR CHICKEN DISEASE CLASSIFICATION BASED ON DUNG IMAGES Andalantama, Yudikha; Hidayat, Tonny; Purwanto, Agus
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 10, No 3 (2025)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v10i3.6490

Abstract

This study examines the application of the ResNet-50 model for categorizing chicken illnesses. The dataset utilized comprises 8,876 samples, which are classified into four main categories: healthy feces, Salmonella, Coccidiosis, and Newcastle disease. The dataset consists of 2,057 samples classified as healthy feces, 2,276 samples classified as Salmonella, 2,103 samples classified as Coc-cidiosis, and 2,440 samples classified as Newcastle disease. The implementation of the ResNet-50 model for analysis showcases outstanding performance, with a classification accuracy of 99.25%. This result affirms the model's exceptional ability to precisely identify poultry illnesses. The results of this study highlight the effectiveness of ResNet-50 in performing complex classification tasks and also provide a basis for future improvements. Considering the exceptional results, there are other aspects that can be improved upon to attain optimal performance. By integrating modern hyperparameter tuning approaches and incorporating diverse supplementary data, the model's generalization is expected to be improved, leading to higher accuracy in many real-world settings. Moreover, this will expand the practical applications of the approach in the veterinary and poultry sectors. This study greatly contributes to the diagnosis of diseases in poultry, relying on the findings obtained. It enables the potential for further progress that can improve the effectiveness of disease detection and prevention.
Comparison of Multilingual Model Sensitivity for Political Fact Verification with Integrated Multi-Evidence Nova Agustina; Kusrini Kusrini; Ema Utami; Tonny Hidayat
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.v7i2.1198

Abstract

Political news is frequently targeted by the dissemination of fake news on social media, which can influence public opinion and undermine trust in democratic processes. The main challenge in addressing this issue lies in the limited sensitivity of cross-lingual fact verification models in capturing semantic relationships between claims and evidence in long-text, multi-evidence settings. Existing approaches often struggle to assess the relevance and quality of evidence, resulting in suboptimal verification performance. This study compares three multilingual Large Language Models (LLMs), namely mBERT, XLM-R, and LaBSE, for political fact verification using an integrated multi-evidence approach. Experiments are conducted on the PolitiFact dataset, with performance evaluated using sensitivity, accuracy, precision, and F1-score metrics.The results indicate that mBERT achieves the highest overall sensitivity at 89.44%, followed by LaBSE at 81.81% and XLM-R at 78.81%. However, mBERT exhibits lower precision, whereas LaBSE provides a better balance between precision (87.02%) and accuracy (86.46%), resulting in an F1-score of 84.33%. XLM-R demonstrates lower sensitivity but maintains competitive precision (85.47%) and accuracy (84.60%), with an F1-score of 82.00%. Sensitivity analysis based on the number of evidence reveals distinct model behaviors, where mBERT performs optimally with six pieces of evidence, XLM-R is more effective under limited evidence conditions, and LaBSE shows a stable and increasing sensitivity trend as the amount of evidence increases, indicating robustness in multi-evidence scenarios. Further statistical analysis shows that XLM-R has the lowest performance variance, while LaBSE statistically outperforms mBERT in several evaluation aspects. Overall, LaBSE is recommended as the most balanced model for multi-evidence-based political fact verification.
OPTIMIZATION OF SOFTWARE DEFECT PREDICTION USING CNN AND ADABOOST: ANALYSIS AND EVALUATION Muhammad Abdul Basit; Arief Setyanto; Tonny Hidayat
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 10, No 3 (2025)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v10i3.6405

Abstract

This study focuses on enhancing software defect prediction (SDP) by integrating Convolutional Neural Networks (CNN) with the AdaBoost algorithm. The PROMISE dataset was employed in this research, and data balancing was achieved using the SMOTE Tomek technique. With the help of AdaBoost, we were able to increase the prediction accuracy after building a complex CNN model to extract features from the da-taset. The AdaBoost model's hyperparameters were fine-tuned using GridSearch to find the best values for enhanced model performance. For the studies, we used StandardScaler to normalize the data after splitting it into training and testing groups with an 80:20 ratio. The ex-perimental results show that compared to the baseline method, SDP's accuracy is significantly improved when CNN, AdaBoost, and GridSearch hyperparameter tweaking are used together. Accuracy, pre-cision, recall, F1 score, MCC, and AUC were some of the measures used to assess the model's performance.
PENERAPAN ALGORITMA RANDOM FOREST DALAM BERBAGAI BIDANG KEILMUAN : SYSTIMATIC LITERATUR REVIEW Riswanto Riswanto; Tonny Hidayat; Asro Nasiri
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 11, No 1 (2026)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v11i1.7316

Abstract

Penerapan teknologi informasi (TI) saat ini, menjadi salah satu aspek penting sebuah perusahaan untuk dapat bersaing di era digital saat ini. Salah satu tolak ukur keberhasilan dalam menerapkan teknologi informasi adalah efektifitas dan efisiensi proses bisnis perusahaan secara menyeluruh. Random forest sebagai salah satu algoritma dalam machine learning yang mempunyai akurasi tinggi dan mampu mengrangi risiko overfitting dipilih untuk menyelesaikan berbagai permasalahan dalam berbgai bidang keilmuan. Melalui metode Systimatic Literature Review (SLR), penelitian ini mengidentifikasi dan menganalisis 55 artikel terkait yang memberikan gambaran mendalam mengenai penerapan algoritma Random Forest dalam berbagai bidang keilmuan. Hasil penelitian menunjukan berbagai tantangan penerapan algoritma random forest dalam berbgai bidang keilmuan, mulai dari optimasi dengan pemilihan berbagai parameter serta mengkombinasikan algoritma random forest dengan algoritma yang lain. Perlakuan tersebut disesuaikan dengan kondisi dan karakteristik data dan objek yang di teliti, dengan harapan diperoleh perhitungan yang paling optimal dan mampu menyelesaikan permasalahan yang terjadi pada bidang tersebut. Dengan demikian, penelitian ini tidak hanya memberikan panduan praktis, akan tetapai memberikan kontribusi pemahaman yang lebih mendalama mengenai peneraan algoritma random forest dalam mengatasi permasalahan dalamberbgai bidang keilmuan
DiG-MFV: Dual-integrated Graph for Multilingual Fact Verification Nova Agustina; Kusrini; Ema Utami; Tonny Hidayat
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 4 (2025): August 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i4.6695

Abstract

The proliferation of misinformation in political domains, especially across multilingual platforms, presents a major challenge to maintaining public information integrity. Existing models often fail to effectively verify claims when the evidence spans multiple languages and lacks a structured format. To address this issue, this study proposes a novel architecture called Dual-integrated Graph for Multilingual Fact Verification (DiG-MFV), which combines semantic representations from multilingual language models (i.e., mBERT, XLM-R, and LaBSE) with two graph-based components: an evidence graph and a semantic fusion graph. These components are processed through a dual-path architecture that integrates the outputs from a text encoder and a graph encoder, enabling deeper semantic alignment and cross-evidence reasoning. The PolitiFact dataset was used as the source of claims and evidence. The model was evaluated by using a data split of 70% for training, 20% for validation, and 10% for testing. The training process employed the AdamW optimizer, cross-entropy loss, and regularization techniques, including dropout and early stopping based on the F1-score. The evaluation results show that DiG-MFV with LaBSE achieved an accuracy of 85.80% and an F1-score of 85.70%, outperforming the mBERT and XLM-R variants, and proved to be more effective than the DGMFP baseline model (76.1% accuracy). The model also demonstrated stable convergence during training, indicating its robustness in cross-lingual political fact verification tasks. These findings encourage further exploration in graph-based multilingual fact verification systems.
FORECASTING STOCK MARKET MODEL: A SYSTEMATIC LITERATURE REVIEW Elia Setiana; Kusrini Kusrini; Tonny Hidayat; Dhani Ariatmanto
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 12 No. 1 (2026): JITK Issue August 2026
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v12i1.7991

Abstract

The increasing digitisation of stock markets and the growing diversity of financial data sources have intensified the need for accurate, robust, and risk-aware stock market forecasting. This systematic literature review synthesises recent evidence to examine the effectiveness of forecasting methods under different data and market conditions, the characteristics of commonly used benchmark datasets, the contribution of preprocessing strategies, and the evaluation and validation practices applied in stock market forecasting. Following the PRISMA framework, 71 peer-reviewed studies retrieved from the Scopus database were systematically screened, classified, and analysed. The evidence mapping shows that sequence-based deep learning models, including LSTM, GRU, and CNN–LSTM, represent the largest methodological group at approximately 41%, followed by transformer- and attention-based approaches at around 16%. Volatility-oriented econometric and classical statistical models account for approximately 18% and 14%, respectively, while probabilistic and quantile-based approaches remain limited. The findings indicate that forecasting performance is strongly context-dependent: classical models remain effective for relatively stationary univariate series, volatility-oriented models are particularly relevant when clustering and spillover effects are present, and deep learning and transformer-based approaches are more suitable for multivariate, nonlinear, and feature-rich settings. Overall, the review highlights the need for greater integration of uncertainty-aware evaluation, regime-sensitive validation, and risk-oriented forecasting frameworks.
Co-Authors AGUS PURWANTO Ainul Yaqin Alexander Phuk Tjilen Alfian, Wahyu Ali Mustopa, Ali Alif Syaiful Huda Alva Hendi Muhammad Alva Hendi Muhammad Andalantama, Yudikha Andi Sunyoto Arief Setyanto Aris Harwanto Arsad Asro Nasiri Bentar Candra P Dewi Anisa Istiqomah Dhani Ariatmanto Ema Utami Ema Utami Firza Septian Ika Asti Astuti Ika Asti Astuti Ilmawati, Fahma Inti JOKO DWI SANTOSO Kusrini Kusrini Kusrini Liana Trihardianingsih M. Ilham Arief Madhika, Yudha Randa Maie Istighosah Mawene, Brenstein Mohammad Rezza Pahlevi Muhammad Abdul Basit Muhammad Anwar Fauzi Mulyatun, Sri Nova Agustina Risna Sari Riswanto Riswanto Safril, Martinus Seniwati, Erni Setiana, Elia Setiawanto, Irwan TEGUH ARIFIANTO, TEGUH Theofanis Orphanoudakis Tutut Dwi Prihatin Zulkarnain, Jefri