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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Jurnal Masyarakat Informatika Jurnas Nasional Teknologi dan Sistem Informasi Jurnal Ekonomi Bisnis dan Kewirausahaan JOIN (Jurnal Online Informatika) Sistemasi: Jurnal Sistem Informasi Journal of Applied Geospatial Information Informatika Mulawarman: Jurnal Ilmiah Ilmu Komputer Sinkron : Jurnal dan Penelitian Teknik Informatika JURNAL MEDIA INFORMATIKA BUDIDARMA JOURNAL OF APPLIED INFORMATICS AND COMPUTING EDUMATIC: Jurnal Pendidikan Informatika Jurnal Teknologi Informasi dan Pendidikan Building of Informatics, Technology and Science Abdimasku : Jurnal Pengabdian Masyarakat Innovation in Research of Informatics (INNOVATICS) Jurnal Teknik Informatika (JUTIF) International Journal of Artificial Intelligence and Robotics (IJAIR) Advance Sustainable Science, Engineering and Technology (ASSET) Devotion: Journal of Research and Community Service JS (Jurnal Sekolah) Jurnal Ekonomi Bisnis dan Kewirausahaan JuTISI (Jurnal Teknik Informatika dan Sistem Informasi)
Sri Winarno
Faculty of Information Science and Technology, Multimedia University
Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Physics Social Sciences Other

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DiabTrack: Sistem Prediksi Dini Diabetes Melitus Tipe 2 berbasis Web menggunakan Algoritma K-Nearest Neighbors Pangestu, Aditya Gilang; Winarno, Sri; Nugraha, Adhitya; Muttaqin, Almas Najiib Imam
Jurnal Pendidikan Informatika (EDUMATIC) Vol 9 No 1 (2025): Edumatic: Jurnal Pendidikan Informatika
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/edumatic.v9i1.29691

Abstract

Type 2 diabetes mellitus is a chronic disease that is often not detected early enough, increasing the risk of serious complications. Based on this, early detection of this disease is very important to reduce its negative impact. This research aims to develop the DiabTrack system, a web-based prediction system using the K-Nearest Neighbors (KNN) algorithm. This type of research is development research using the Rapid Application Development (RAD) model, including the requirements planning, design workshop, and implementation stages. The dataset used comes from Kaggle, containing 53,000 samples and 8 features. The model is trained using the KNN algorithm and the SMOTE technique to balance the data. Evaluation results show that the KNN model achieves an accuracy of 99.17%, a recall of 100%, and an F1-score of 94%, making it the chosen algorithm for the DiabTrack website. Additionally, Black Box testing results indicate that all features in the DiabTrack system function as expected, helping the public monitor their health conditions while serving as an initial analysis tool for medical professionals.
Pendekatan Multi-Input dalam Deteksi Kanker Kulit: Implementasi EfficientNetV2-B2 dan LightGBM Ibad, M. Azka Khoirul; Winarno, Sri
Jurnal Pendidikan Informatika (EDUMATIC) Vol 9 No 1 (2025): Edumatic: Jurnal Pendidikan Informatika
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/edumatic.v9i1.29771

Abstract

Skin cancer is one of the types of cancer with a high prevalence rate, so early detection is very important to increase the chances of recovery. This study aims to develop a skin cancer detection model that combines image data and tabular data using EfficientNetV2-B2 for image feature extraction and LightGBM for tabular data prediction estimation. The ISIC 2024 dataset used consists of 401,059 images of skin lesions with tabular features, including age, gender, location, diameter, and shape of the lesions. Tabular data is processed with normalization and encoding to avoid bias. Image data is also processed with augmentation techniques from kerascv. This multi-input model combines image and tabular features using concatenation techniques, with a dense layer as the final output. Our findings show that the model's accuracy and AUC value reached 96% and 98%, with success in handling class imbalance using undersampling and oversampling techniques. This study shows that the combination of images and tabular data increases the accuracy of skin cancer detection by 2%, compared to conventional CNN models, which only achieve an accuracy of around 94%. Moreover, this model offers better computational efficiency compared to conventional CNN models. The main contribution of this research is the use of multi-input that complements visual information with clinical data for more accurate and efficient skin cancer detection.
Enhanced Image Security through 4D Hyperchaotic System and Hybrid Key Techniques Farandi, Muhammad Naufal Erza; Winarno, Sri; Fauzyah, Zahrah Asri Nur
Sistemasi: Jurnal Sistem Informasi Vol 13, No 6 (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.v13i6.4675

Abstract

This study develops a digital image encryption method using a 4D hyperchaotic system combined with a hybrid key to maximize data security. By generating a random and uniform pixel distribution, the method makes decryption significantly harder for unauthorized access. Evaluations are conducted through histogram analysis, robustness tests, NPCR, UACI, and information entropy. The findings reveal that the method effectively breaks pixel correlation, rendering the encrypted image unrecognizable. Histogram analysis confirms a uniform pixel distribution, while robustness tests show the system can maintain image quality despite manipulations or attacks. NPCR and UACI tests highlight the method’s high sensitivity to even minor changes in the original image, further enhancing security. Information entropy demonstrates a higher level of randomness compared to other encryption techniques. This 4D hyperchaotic and hybrid key-based approach holds considerable promise for applications requiring highly secure image transmission and storage, ensuring reliable data protection in sensitive environments.
PENINGKATAN MINAT BELAJAR SISWA SEKOLAH DASAR MELALUI MODEL MAKE A MATCH PADA MATA PELAJARAN MATEMATIKA Ayu Harini, Pradhita Rizka; Desstya, Anatri; Kuswidiani, Erika Widya; Winarno, Sri
JS (JURNAL SEKOLAH) Vol. 8 No. 4 (2024): SEPTEMBER 2024
Publisher : Universitas Negeri Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24114/js.v8i4.63342

Abstract

Abstract: This research aims to explore the application of learning models Make a Match in increasing second grade students' interest in learning in mathematics subjects in elementary schools. This type of research uses classroom action research with research subjects as second grade students. There are two cycles in this research. The data analysis technique used is quantitative descriptive analysis. The data collection method used was observation using a learning interest observation sheet and documentation in the form of a recap of teacher grades. The research results show that the model Make a Match can increase students' interest in learning. The increase in students' interest in learning is shown by the percentage increase from cycle 1 of 54.63% to 86.97% in cycle II.Keyword: Make A Match, Interest in Learning, MathematicsAbstrak: Penelitian ini bertujuan untuk mengeksplorasi penerapan model pembelajaran Make a Match dalam meningkatkan minat belajar siswa kelas II pada mata pelajaran matematika di sekolah dasar. Jenis penelitian ini menggunakan penelitian tindakan kelas dengan subjek penelitian siswa kelas II. Penelitian ini terdapat dua siklus. Teknik analisis data yang digunakan adalah analisis desktiptif kuantitatif. Metode pengumpulan data yang digunakan adalah observasi dengan menggunakan lembar observasi minat belajar dan dokumentasi berupa rekap nilai guru. Hasil penelitian menunjukkan bahwa model Make a Match dapat meningkatkan minat belajar siswa. Peningkatan minat belajar siswa ditunjukkan dengan kenaikan presentase dari siklu s 1 sebesar 54,63% menjadi 86,97% di siklus II. Kata Kunci: Make A Match, Minat Belajar, Matematika
MOTIVASI KERJA, LINGKUNGAN KERJA, DAN GAYA KEPEMIMPINAN UNTUK MENINGKATKAN PRODUKTIVITAS KERJA KARYAWAN DI LINGKUNGAN YAYASAN PENDIDIKAN PERGURUAN TINGGI SAHID SURAKARTA Hastuti, Tri Puji; Winarno, Sri; Cahyani, Rusnandari Retno
JURNAL EKONOMI BISNIS DAN KEWIRAUSAHAAN Vol 6 No 2 (2017): Jurnal Ekonomi Bisnis DAN Kewirausahaan Vol. 6, No. 2 Agustus 2017
Publisher : Universitas Sahid Surakarta

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

Abstract

Dalam era persaingan usaha yang semakin ketat, kinerja yang dimiliki karyawan dituntut untuk terus meningkat. Salah satu langkah untuk mempertahankan atau meningkatkan kinerja karyawan dapat dilakukan dengan mengevaluasi kinerja karyawan dan melakukan serangkaian perbaikan agar selalu meningkatkankualitas karyawan tersebut sehingga perusahaan tumbuh dan unggul dalam persaingan, atau minimal tetap dapat bertahan. Penelitian ini menggunakan teknik pengambilan sampel (sampling) purposive sampling dengan kuesioner yang disebar sebanyak 100 mengunakan variabel motivasi kerja sebesar 72,9% atas jawaban setuju pada pertanyaan kelima yaitu setiap saya mendapat kesulitan, rekan kerja mau memberikan bantuan kepada saya. Hal ini menunjukkan bahwa kerja sama antar karyawan di lingkungan yayasan pendidikan perguruan tinggi Sahid Surakarta sangat baik. lingkungan kerja hasil terbanyak sebesar 72,9% atas jawaban setuju dengan pertanyaan hubungan saya dengan karyawan lain harmonis.
Integrasi Convolutional Autoencoder dengan Support Vector Machine untuk Klasifikasi Varietas Almond Fadlullah, Rizal; Winarno, Sri; Naufal, Muhammad
Jurnal Teknik Informatika dan Sistem Informasi Vol 11 No 1 (2025): JuTISI
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v11i1.9738

Abstract

This research aims to optimize almond variety classification by integrating Convolutional Autoencoder (CAE) as a feature extraction method and Support Vector Machine (SVM) for classification. The research process includes data collection from available datasets, preprocessing, and splitting data for training and testing. Features from almond images are extracted using CAE, which are then used in the SVM model for classification. Model evaluation shows a classification accuracy of 97% on the test data, a significant increase compared to the 48% accuracy of conventional SVM. The CAE-SVM approach offers more compact and informative feature representations, effectively improving almond variety recognition. This study highlights the potential of combining CAE and SVM advantages to enhance plant image analysis and encourages further advancements in machine learning applications in agriculture.
Optimasi Algoritma Decision Tree Menggunakan GridSearchCV untuk Klasifikasi Tipe Obesitas Laurent, Feby; Winarno, Sri; Dewi, Ika Novita
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.v7i3.8638

Abstract

The rise in obesity cases in various countries, including Indonesia, has become a serious public health problem because it increases the risk of chronic diseases and affects individuals' psychological aspects. One of the main challenges in obesity management is the differences in obesity types in each individual, which are influenced by various factors. Therefore, accurate classification methods are needed to ensure more targeted treatment. In this context, machine learning-based technology is a potential solution for classifying obesity types. However, variations in individual characteristics make the classification process complex, as models often struggle to accurately distinguish obesity types. To overcome this problem, the Decision Tree algorithm was chosen because of its easy-to-interpret results. However, using Decision Tree with default parameters on datasets with many attributes and high variation tends to cause overfitting and decrease accuracy. Furthermore, Decision Tree performance is highly dependent on hyperparameter settings, requiring optimization techniques to achieve optimal results. Based on this, this study aims to optimize the Decision Tree algorithm using GridSearchCV to obtain the most optimal parameters to improve model performance in obesity type classification. The dataset used is from the UCI Machine Learning Repository, consisting of 2,111 rows of data and 17 attributes. Based on the initial test results, the default model achieved 92.58% accuracy, 92.58% recall, 92.66% precision, and 92.56% F1-score. After optimization, the accuracy increased to 95.69%, 95.69% recall, 95.72% precision, and 95.67% F1-score. The 3.1% increase in accuracy demonstrates the effectiveness of GridSearchCV in improving Decision Tree performance, resulting in a more accurate and stable prediction model. This research is expected to contribute as a basis for decision-making in early detection and prevention and treatment of obesity more efficiently and effectively.
Comprehensive Benchmark of Yolov11n, SSD MobileNet, CenterFace, Yunet, FastMtCnn, HaarCascade, and LBP for Face Detection in Video Based Driver Drowsiness Go, Agnestia Agustine Djoenaidi; Alzami, Farrikh; Naufal, Muhammad; Azies, Harun Al; Winarno, Sri; Pramunendar, Ricardus Anggi; Megantara, Rama Aria; Maulana, Isa Iant; Arif, Mohammad
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.v7i3.8678

Abstract

Face detection is a critical foundation of video-based drowsiness monitoring systems because all downstream tasks such as eye-closure estimation, yawning detection, and head movement analysis depend entirely on correctly identifying the face region. Many previous studies rely on detector-generated outputs as ground truth, which can introduce bias and inflate model performance . To avoid this limitation, I manually constructed a ground truth dataset using 1,229 frames extracted from 129 yawning and microsleep videos in the NITYMED dataset. Ten representative frames were sampled from each video using a face-guided extraction script, and all frames were manually annotated in Roboflow following the COCO format to ensure accurate bounding box labeling under varying lighting, head poses, and facial deformation. Using this manually annotated dataset, I conducted a comprehensive benchmark of seven face-detection algorithms: YOLOv11n, SSD MobileNet, CenterFace, YuNet, FastMtCnn, HaarCascade, and LBP. The evaluation focused on localization quality using Intersection over Union (IoU ≥ 0.5) and Dice Similarity, allowing each algorithm’s predicted bounding box to be directly compared against human defined ground truth. The results show that HaarCascade achieved the highest IoU and Dice scores, particularly in frontal and well-lit frames. FastMtCnn also produced strong alignment with a high number of correctly matched frames. CenterFace and SSD MobileNet demonstrated smooth bounding box fitting with competitive Dice scores, while YOLOv11n and YuNet delivered moderate but stable performance across most samples. LBP showed the weakest results, mainly due to its sensitivity to lighting variations and soft-texture regions. Overall, this benchmark provides an unbiased and comprehensive comparison of modern and classical face-detection algorithms for video-based driver-drowsiness applications.
Addressing Extreme Class Imbalance in Multilingual Complaint Classification Using XLM-RoBERTa Ariyanto, Muhammad; Alzami, Farrikh; Sani, Ramadhan Rakhmat; Gamayanto, Indra; Naufal, Muhammad; Winarno, Sri; Iswahyudi
Journal of Applied Informatics and Computing Vol. 10 No. 1 (2026): February 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i1.11606

Abstract

Government complaint management systems often suffer from extreme class imbalance, where a few public service categories accumulate most reports while many others remain under-represented. This research examines whether simple class weighting can improve fairness in multilingual transformer models for automatic routing of Indonesian citizen complaints on the LaporGub Central Java e-governance platform. The dataset comprises 53,877 Indonesian-language complaints spanning 18 service categories with an imbalance ratio of about 227:1 between the largest and smallest classes. After cleaning and deduplication, we stratify the data into training, validation, and test sets. We compare three approaches: (i) a linear support vector machine (SVM) with term frequency inverse document frequency (TF-IDF) unigram and bigram and class-balanced weights, (ii) a cross-lingual RoBERTa (XLM-RoBERTa-base) model without class weighting, and (iii) an XLM-RoBERTa-base model with a class-weighted cross-entropy loss. Fairness is operationalised as equal importance for categories and quantified primarily using the macro-averaged F1-score (Macro-F1), complemented by per-class F1, weighted F1, and accuracy. The unweighted XLM-RoBERTa model outperforms the SVM baseline in Macro-F1 (0.610 vs 0.561). The class-weighted variant attains similar Macro-F1 (0.608) while redistributing performance towards minority categories. Analysis shows that class weighting is most beneficial for categories with a few hundred to several thousand samples, whereas extremely rare categories with fewer than 200 complaints remain difficult for all models and require additional data-centric interventions. These findings demonstrate that multilingual transformer architectures combined with simple class weighting can provide a more balanced backbone for automated complaint routing in Indonesian e-government, particularly for low- and medium-frequency service categories.
Multivariate LSTM-Based Intraday Gold Price Prediction with Rolling Time Series Validation Arif, Mohammad; Alzami, Farrikh; Fahmi, Amiq; Udayanti, Erika Devi; Naufal, Muhammad; Winarno, Sri; Malim, Nurul Hashimah Ahmad Hassain; Yosep Teguh Sulistyono, Marcelinus
Jurnal Masyarakat Informatika Vol 17, No 1 (2026): May 2026 (Ongoing)
Publisher : Department of Informatics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/jmasif.17.1.78091

Abstract

Projecting XAUUSD (gold vs. US dollar) prices on a one-hour interval is particularly challenging due to the market's dynamic and nuanced character. To address short-term financial forecasting, an advanced deep learning methodology utilizing Long Short-Term Memory (LSTM) models was employed. Historical XAUUSD data for 2024 was resampled to hourly intervals and supplemented with SMA, RSI, MACD, and Bollinger Bands to understand the market structure better. An LSTM model was developed using open, high, low, and close prices as inputs, with the close price designated as the output target. Data normalization was performed via MinMaxScaler. The model was validated using Time Series Cross-Validation (TSCV) with a rolling origin expanding window over five splits—a sophisticated method for evaluating performance. The results demonstrated the LSTM model's capability, showcasing a mean RMSE of 9.9574, a mean MAE of 7.4411, an R² score of 0.9535, and a remarkably low MAPE of 0.3009%. These findings indicate the advanced model effectively predicts intraday prices, even while grappling with complex and nonlinear patterns, offering a powerful instrument for trading professionals and researchers to cut through market noise.
Co-Authors Abiyyi, Ryandhika Bintang Adhitya Nugraha Al-Azies, Harun Alzami, Farrikh Anatri Desstya Andrean, Muhammad Niko Ardytha Luthfiarta Arga Retha, Helynda Mulya Ariansyah, M. Hafidz ARIYANTO, MUHAMMAD Aryanti, Firda Ayu Dwi Asih Rohmani, Asih Atha Rohmatullah, Fawwaz Ayu Harini, Pradhita Rizka Cahya, Leno Dwi Cahyani, Ailsa Nurina Edi Noersasongko Erika Devi Udayanti Fadlullah, Rizal Fahmi Amiq Fahri Firdausillah Farandi, Muhammad Naufal Erza Fauzyah, Zahrah Asri Nur Fikri Budiman Firmansyah, Gustian Angga Fitri, Maulatus Shaffira Ganiswari, Syuhra Putri Go, Agnestia Agustine Djoenaidi Guruh Fajar Shidik Harun Al Azies Hastuti, Tri Puji Ibad, M. Azka Khoirul Ika Novita Dewi Indra Gamayanto Iswahyudi Junta Zeniarja Kamarudin, Fatkhurridlo Pranoto Khoirunnisa, Emila Krisna, Julius Immanuel Theo Kurniawan, Defri Kuswidiani, Erika Widya Laksono, Giffari Ilham Laurent, Feby Malim, Nurul Hashimah Ahmad Hassain Maulana, Isa Iant Maulani, Ahmad Alaik Megantara, Rama Aria Mohammad Arif Muhammad Naufal Muttaqin, Almas Najiib Imam Nur Fitri, Esmi Pangestu, Aditya Gilang Pratama, Raffy Nicandra Putra Pratama, Rifky Ariya Pulung Nurtantio Andono Putra, Permana Langgeng Wicaksono Ellwid Putri, Rusyda Tsaniya Eka Ramadhan Rakhmat Sani Ramadhani, Talitha Olga Ricardus Anggi Pramunendar Rizqi, Ainur Rahma Miftakhul Rofiqi, Harri Kurniawan Rony, Zahara Tussoleha Rusnandari Retno Cahyani Sabilillah, Ferris Tita Sasono Wibowo Senata, Denny Soeleman, M. Arief Soeroso, Dennis Adiwinata Irwan Sukamto, Titien Suhartini Sulistyono, Teguh Syifa Nurazizah, Syifa Wardhana, Faviola Proba Widhiyanti, Erna Amalia Wijaya, Tan Nicholas Octavian Yosep Teguh Sulistyono, Marcelinus Yudantiar, Mayang Arinda Zami, Farrikh Al