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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Jurnal Sarjana Teknik Informatika Jurnal Simantec Jurnal Informatika dan Teknik Elektro Terapan International conference on Information Technology and Business (ICITB) JURNAL INSTEK (Informatika Sains dan Teknologi) Jiko (Jurnal Informatika dan komputer) JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Jurnal Teknologi Terpadu JASIEK (Jurnal Aplikasi Sains, Informasi, Elektronika dan Komputer) bit-Tech JATI (Jurnal Mahasiswa Teknik Informatika) International Journal of Advances in Data and Information Systems Jifosi Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) Jurnal Diferensial Jurnal Informatika Teknologi dan Sains (Jinteks) Makara Journal of Technology Malcom: Indonesian Journal of Machine Learning and Computer Science J-Icon : Jurnal Komputer dan Informatika Jurnal Sistem Informasi dan Informatika Indonesian Journal on Data Science Emerging Statistics and Data Science Journal Signal and Image Processing Letters
Wahyu Syaifullah Jauharis Saputra
Universitas Pembangunan Nasional Veteran Jawa Timur
Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mathematics Mechanical Engineering Public Health

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Indonesian Sign Language (BISINDO) Classification Using Xception Transfer Learning Architecture Amelia, Meisya Vira; Saputra, Wahyu Syaifullah Jauharis; Hindrayani, Kartika Maulida; Riyantoko, Prismahardi Aji
International Journal of Advances in Data and Information Systems Vol. 6 No. 2 (2025): August 2025 - International Journal of Advances in Data and Information Systems
Publisher : Indonesian Scientific Journal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59395/ijadis.v6i2.1392

Abstract

Human communication generally relied on speech. However, this was not applicable to the deaf people, who depended on sign language for daily interactions. Unfortunately, not everyone had the ability to understand sign language. In higher education environments, the lack of individuals proficient in sign language often created inequality in the learning process for deaf students. This limitation could be addressed by fostering a more inclusive environment, one of which was through the implementation of a sign language translation system. Therefore, this study aimed to develop a machine learning model capable of detecting and translating Indonesian Sign Language (BISINDO) alphabet gestures. The model was built using the Xception transfer learning method from Convolutional Neural Networks (CNN). The dataset consisted of 26 BISINDO alphabet gestures with a total of 650 images. The model was evaluated using K-Fold cross-validation and achieved an F1-score of 94% during testing.
PERBANDINGAN ARSITEKTUR VANILLA, STACKED, DAN BIDIRECTIONAL LONG SHORT-TERM MEMORY UNTUK PREDIKSI PERIODE MUSIM DI SURABAYA Sinthya Putri, Diana; Syaifullah Jauharis Saputra, Wahyu; Maulida Hindrayani, Kartika
JATI (Jurnal Mahasiswa Teknik Informatika) Vol. 9 No. 3 (2025): JATI Vol. 9 No. 3
Publisher : Institut Teknologi Nasional Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36040/jati.v9i3.13769

Abstract

Indonesia memiliki dua musim utama, namun perubahan iklim global menyebabkan pergeseran pola musim yang tidak menentu. Hal ini berdampak pada berbagai sektor, termasuk agribisnis, transportasi, dan konstruksi. Surabaya, sebagai pusat ekonomi di Jawa Timur, memerlukan prediksi musiman yang akurat untuk mitigasi risiko dan perencanaan strategis. Penelitian ini mengevaluasi kinerja tiga arsitektur Long Short-Term Memory (LSTM), yaitu Vanilla LSTM, Stacked LSTM, dan Bidirectional LSTM, dalam memprediksi pola musiman curah hujan di Surabaya. Data yang digunakan berasal dari BMKG Stasiun Meteorologi Maritim Tanjung Perak, mencakup periode 2001-2024. Hasil eksperimen menunjukkan bahwa Bidirectional LSTM mencapai nilai MAE terendah sebesar 25,7883, diikuti oleh Stacked LSTM dengan MAE 26,5515, dan Vanilla LSTM dengan MAE 27,7023. Temuan ini mengkonfirmasi bahwa arsitektur yang lebih dalam dan kompleks, seperti Stacked LSTM dan Bidirectional LSTM, mampu meningkatkan akurasi prediksi secara signifikan dibandingkan Vanilla LSTM.
ANALYSIS AND PREDICTION OF MOTOR VEHICLE CARBON DIOXIDE EMISSIONS USING A HYBRID LSTM AND ARIMA ALGORITHM Muhammad Hakam Fardana; Wahyu Syaifullah Jauharis Saputra; Made Hanindia Prami Swari
Jurnal Informatika dan Teknik Elektro Terapan Vol. 13 No. 3 (2025)
Publisher : Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jitet.v13i3.6782

Abstract

CO2 emissions from motor vehicles contribute substantially to climate change. Accurate prediction of emission trends is thus crucial for mitigation strategies. This research evaluates the performance of a Hybrid Long Short-Term Memory (LSTM) and Autoregressive Integrated Moving Average (ARIMA) model for predicting Motor Vehicle CO2 Emissions. This hybrid model integrates ARIMA's capability in handling linear patterns and LSTM's in capturing long-term non-linear dependencies. Using 1000 historical data entries from the Eco-Route Application, the hybrid model was tested and compared with single models. Results show the hybrid model achieved good prediction accuracy with MAE 0.0941, MAPE 10.20%, and RMSE 0.1081 in its best scenario. However, on this specific dataset, the single ARIMA model demonstrated the best overall performance (MAE 0.0835, MAPE 9.33%, RMSE 0.0975). Dataset limitations were identified as affecting the hybrid's capability. The Hybrid LSTM-ARIMA model is determined to be a promising option for CO2 emission prediction, especially when larger datasets are available.
Optimization of facial recognition authentication system using InceptionResNetV1 with Pretrained VGGFACE2 Gunawan, Ellexia Leonie; Mas Diyasa, I Gede Susrama; Jauharis Saputra, Wahyu Syaifullah
Jurnal Simantec Vol 13, No 2 (2025): Jurnal Simantec Juni 2025
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/simantec.v13i2.29776

Abstract

Face recognition as a biometric authentication method continues to evolve due to its high security and ease of use. However, training models from scratch faces challenges such as the need for large datasets and high computational resources. This study aims to optimize the face authentication system using the InceptionResNetV1 architecture with a transfer learning approach from the pretrained VGGFace2 model and to compare its performance with CASIA-WebFace. Face detection is conducted using YOLOv8, face embeddings are generated by InceptionResNetV1, and authentication is performed by calculating the Euclidean distance between embeddings. Face data were collected from university students and divided into training and testing datasets. Performance evaluation includes accuracy, precision, recall, F1-score, and the confusion matrix. The results show that the VGGFace2 model achieved an accuracy of 98.75%, a recall of 100%, and an F1-score of 99.26%, with no False Negatives, while CASIA-WebFace achieved an accuracy of 86.25% with a recall of 85.07%. The main contribution of this study is to demonstrate that the use of transfer learning with the pretrained VGGFace2 model can significantly improve the accuracy of face authentication systems and to show its effectiveness for developing systems with limited data and computational resources. This study contributes by highlighting the superiority of the pretrained VGGFace2 model in face authentication systems and emphasizing the effectiveness of transfer learning for implementing accurate systems under resource constraints.Keywords: Authentication System, InceptionResNetV1, Face Recognition, Transfer Learning, VGGFace2
Classification of Road Damage in Sidoarjo Using CNN Based on Inception Resnet-V2 Architecture Zahrah, Fathima; Diyasa, I Gede Susrama Mas; Saputra, Wahyu Syaifullah Jauharis
Signal and Image Processing Letters Vol 7, No 1 (2025)
Publisher : Association for Scientific Computing Electrical and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/simple.v7i1.123

Abstract

Road damage is a serious issue in Sidoarjo Regency, posing risks to road users' safety. This study aims to classify road surface conditions using a Convolutional Neural Network (CNN) model based on the Inception ResNet-V2 architecture. The research develops an image-based classification model by combining secondary data from Kaggle and primary data obtained through Google Street View API scraping, along with training strategies such as data augmentation, class balancing, early stopping, and model checkpointing. A total of 885 images were used, categorized into three classes: potholes, cracks, and undamaged roads. The model was trained over 20 epochs with early stopping triggered at epoch 15, when validation accuracy reached 95.95%. Evaluation on the test set showed a test accuracy of 83%. The undamaged road class achieved the highest performance with an F1-score of 0.89, while the pothole class recorded an F1-score of 0.79. The lowest performance was observed in the cracked road class, with an F1-score of 0.65, indicating the model's limited ability to detect fine crack features. This limitation is likely due to class imbalance and visual similarity between classes. Although the model demonstrated good generalization for the two majority classes, the performance gap between validation and test accuracy highlights the need to improve detection for minority classes. Future work is recommended to explore advanced augmentation techniques, increase the representation of minority class data, and consider alternative architectures or ensemble methods to enhance the model’s sensitivity to subtle road damage features.
Prediksi Laju Inflasi di Jawa Timur Menggunakan Model N-BEATS dan Optimasi Optuna: Prediction of Inflation Rate in East Java Using the N-BEATS Model and Optuna Optimization Riswanda, Mohammad Nizar; Trimono, Trimono; Saputra, Wahyu Syaifullah Jauharis
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 5 No. 3 (2025): MALCOM July 2025
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v5i3.2141

Abstract

Inflasi merupakan indikator penting yang memengaruhi kestabilan dan pertumbuhan ekonomi suatu wilayah. Prediksi inflasi yang akurat sangat dibutuhkan guna mendukung perumusan kebijakan ekonomi yang tepat. Penelitian ini mengusulkan penggunaan model N-BEATS (Neural Basis Expansion Analysis for Time Series) yang dioptimalkan dengan Optuna untuk memprediksi inflasi di Provinsi Jawa Timur. Data yang digunakan berupa deret waktu univariat, yaitu laju inflasi bulanan dari Januari 2005 hingga Desember 2024, yang diperoleh dari Badan Pusat Statistik (BPS). Evaluasi performa model dilakukan menggunakan metrik Mean Absolute Percentage Error (MAPE). Berbeda dengan model tradisional seperti ARIMA dan LSTM, N-BEATS mengandalkan jaringan saraf feedforward dengan arsitektur blok residual yang mampu melakukan rekonstruksi masa lalu (backcast) dan prediksi masa depan (forecast). Optimasi hyperparameter melalui Optuna berhasil meningkatkan akurasi model secara signifikan. Hasil Penelitian menunjukkan bahwa N-BEATS teroptimasi mencapai MAPE sebesar 8,97%, lebih baik dibandingkan N-BEATS dasar (11,05%), ARIMA (16,95%), dan LSTM (12,23%). Temuan ini mengindikasikan bahwa pendekatan N-BEATS dengan Optuna efektif dalam meningkatkan akurasi prediksi inflasi dan dapat menjadi alat bantu penting bagi perencanaan ekonomi di tingkat daerah.
Klasterisasi Produktivitas Daerah di Jawa Tengah Berdasarkan Ketenagakerjaan Menggunakan K-Means dan Average Linkage Nashrullah, Ahmad Firqi; Mahardhika, Rivaldi Dwi; Rusdiyanto, Nur Rahmat; May Wara, Shindi Shella; Saputra, Wahyu Syaifullah Jauharis
JURNAL DIFERENSIAL Vol 7 No 2 (2025): November 2025
Publisher : Program Studi Matematika, Universitas Nusa Cendana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35508/jd.v7i2.22516

Abstract

This study employs K-Means and Agglomerative Clustering (Average Linkage) to group regions based on variables such as the number of residents, unemployment rate, and other supporting indicators. The data are normalized and evaluated using the Silhouette Score metric, yielding three optimal clusters. Average Linkage (0.3596) outperforms K-Means (0.2627). The Average Linkage results indicate that cluster 1 is characterized by stable productivity and low unemployment, cluster 2 consists solely of Semarang City with the highest Human Development Index and wages, and cluster 3 comprises underdeveloped areas with high unemployment and low wages. This clustering is highly beneficial for supporting more targeted data-driven regional development policies.
Optimization of Palm Fruit Ripeness Detection With Yolov11 on CPU Anniswa, Iqbal Ramadhan; JAUHARIS SAPUTRA, Wahyu Syaifullah; Idhom, Mohammad; Rizaldy Pratama, Alfan; Susrama Mas Diyasa, I Gede
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 19, No 4 (2025): October
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.111253

Abstract

The palm oil industry is one of the strategic sectors that contributes significantly to the Indonesian economy. However, this industry still faces various challenges, particularly in terms of operational efficiency and the implementation of digitalization, especially at the level of independent farmers who often still use manual methods to determine the ripeness of the fruit. This manual process is prone to subjectivity, which can impact harvest quality and supply chain efficiency. To address this issue, this study proposes a palm oil fruit ripeness detection system based on the YOLOv11 algorithm, chosen for its advantages in inference speed and detection accuracy, especially when run on devices with limited resources. The developed model was then implemented using the ONNX Runtime Framework. This enables accelerated inference processes and supports portability on hardware with limited resources. Test results show that the model achieves an mAP@50 accuracy of 90.2% with an average latency of around 255 ms to 300 ms. With these achievements, this system is not only reliable in detecting fruit ripeness, but also efficient in processing time and relevant to support digital transformation in the palm oil plantation sector.
Segmentasi Wilayah Berdasarkan Indikator Kesehatan Lingkungan dan Akses Pelayanan Dasar di Provinsi Jawa Timur: Segmentasi Wilayah Berdasarkan Indikator Kesehatan Lingkungan dan Akses Pelayanan Dasar di Provinsi Jawa Timur Abdillah, Indah Rahma; Diana Novitasari; Amellia Harmaimun Hidayah; Shindi Shella May Wara; Wahyu Syaifullah Jauharis Saputra
Emerging Statistics and Data Science Journal Vol. 3 No. 3 (2025): Emerging Statistics and Data Science Journal
Publisher : Statistics Department, Universitas Islam Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20885/esds.vol3.iss.3.art24

Abstract

Upaya peningkatan kesehatan lingkungan dan pelayanan dasar memerlukan pemahaman yang mendalam terhadap karakteristik wilayah. Provinsi Jawa Timur, dengan keragaman kondisi antar Kabupaten/Kota, menjadi contoh penting dalam analisis ini. Pengelompokan wilayah dilakukan berdasarkan tujuh indikator, yaitu akses air minum layak, akses sanitasi layak, kepemilikan jamban, kasus diare, keluhan kesehatan, kepadatan penduduk, dan jumlah puskesmas. Metode Hierarchical Agglomerative Clustering (HAC) dan K-Means Clustering diterapkan untuk membentuk klaster wilayah yang homogen. Evaluasi performa klasterisasi menggunakan Silhouette Score, Calinski-Harabasz Index, dan Dunn Index menunjukkan bahwa HAC menghasilkan segmentasi yang lebih optimal. Analisis menghasilkan lima klaster wilayah dengan karakteristik berbeda: (1) kabupaten dengan kepadatan sedang, sanitasi terbaik, namun kasus diare dan keluhan kesehatan tinggi; (2) kabupaten dengan sanitasi rendah namun kasus diare dan keluhan kesehatan rendah; (3) kota dengan kepadatan sangat tinggi, sanitasi baik, namun fasilitas kesehatan terbatas; (4) kawasan metropolitan dengan kasus diare sangat tinggi akibat sanitasi buruk; (5) kabupaten dengan kepadatan rendah, akses air minum rendah, dan sanitasi cukup baik. Temuan ini memberikan dasar bagi pengembangan strategi intervensi kesehatan lingkungan yang lebih tepat sasaran.
Analisis Sentimen Kepuasan Pengguna OYO DiPlaystore Dengan Multinoial Naive Bayes dan Chi-square Aziz, Rizky; Tresna Maulana Fahrudin; Wahyu Syaifullah Jauharis Saputra
JURNAL FASILKOM Vol. 14 No. 1 (2024): Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer)
Publisher : Unversitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/jf.v14i1.6943

Abstract

ABSTRACKOpinions play a crucial role in everyday life, significantly influencing human behavior and decisions. Especially in the context of business and organizations, consumer opinions about products and services are highly valuable. This study focuses on analyzing the sentiment of OYO application reviews on the Google Play Store, with the goal of classifying reviews as either positive or negative. OYO Hotels & Homes, a startup company in the accommodation sector originating from India, has achieved remarkable success with revenues reaching US$951 million in fiscal year 2019. The primary classification method used is Multinomial Naïve Bayes, which is an approach in supervised learning, along with Chi-Square feature selection to explore correlations between factors influencing user satisfaction. The research process includes data collection of reviews, preprocessing, labeling, and data splitting. Subsequently, TF-IDF weighting and Chi-Square feature selection are performed. The results of sentiment analysis indicate a dominance of positive reviews, reflecting user satisfaction with OYO services. The classification process uses the Multinomial Naïve Bayes algorithm, with an accuracy rate of 85.5% without feature selection, increasing to 87.00% with Chi-Square feature selection. These results demonstrate the effectiveness of the Multinomial Naïve Bayes algorithm and the importance of feature selection in sentiment analysis. Through a deeper understanding of user sentiment, companies can enhance service quality and respond to feedback more effectively, ensuring optimal customer satisfaction. This research has broad implications for sentiment analysis and the use of statistical methods to address complex issues in the technology industry. Keywords: Sentiment Analysis, OYO Application, Google Playstore, Multinomial Naïve Bayes, Chi-Square Feature Selection. Abstrak Opini memainkan peran krusial dalam kehidupan sehari-hari, memengaruhi perilaku dan keputusan manusia secara signifikan. Terutama dalam konteks bisnis dan organisasi, pendapat konsumen tentang produk dan layanan sangatlah berharga. Penelitian ini berfokus pada analisis sentimen ulasan aplikasi OYO di Google Playstore, dengan tujuan mengklasifikasikan ulasan menjadi positif atau negatif. OYO Hotels & Homes, sebuah perusahaan startup di sektor akomodasi yang berasal dari India, telah mencapai kesuksesan luar biasa dengan pendapatan mencapai US$951 juta pada tahun fiskal 2019. Metode klasifikasi utama yang digunakan adalah Multinomial Naïve Bayes, yang merupakan pendekatan dalam pembelajaran terawasi dan seleksi fitur Chi-Square untuk mengeksplorasi korelasi antara faktor-faktor yang memengaruhi kepuasan pengguna. Proses penelitian meliputi pengumpulan data ulasan, preprocessing, labeling, dan pembagian data. Selajutnya dilakukan pembobotan TF-IDF dan seleksi fitur Chi-Square. Hasil analisis sentimen memperlihatkan dominasi ulasan positif, menunjukkan kepuasan pengguna terhadap layanan OYO. Proses klasifikasi menggunakan algoritma Multinomial Naïve Bayes, dengan hasil akurasi model tanpa seleksi fitur sebesar 85.5%, meningkat menjadi 87.00% dengan seleksi fitur Chi-Square. Hasil ini menunjukkan efektivitas algoritma Multinomial Naïve Bayes dan pentingnya seleksi fitur dalam analisis sentimen. Melalui pemahaman yang lebih dalam terhadap sentimen pengguna, perusahaan dapat meningkatkan kualitas layanan dan merespons umpan balik dengan lebih baik, memastikan kepuasan pelanggan yang optimal. Penelitian ini memiliki implikasi luas dalam analisis sentimen dan penggunaan metode statistik untuk mengatasi masalah kompleks dalam industri teknologi.
Co-Authors Abdillah, Indah Rahma Agus Arifin, Agus Agus Zainal Arifin Ahmad Adiib Aminullah Alfan Rizaldy Pratama Alfath Daryl Alhajir Alyssa Amorita Azzah Amelia, Meisya Vira Amellia Harmaimun Hidayah Aminullah, Ahmad Adiib Amri Muhaimin Andry Syva Maldini Anniswa, Iqbal Ramadhan Anny Yuniarti Aurora Prameswaty, Almira Aviolla Terza Damaliana Aviolla Terza Damaliana Aziz, Rizky Dandi Nur Faizi Diana Novitasari Diyasa, I Gede Susrama Mas Dwi Arman Prasetya Dwi Arman Prasetya Eva Yulia Puspaningrum Faris Nur Tsani Firza Prima Aditiawan Gunawan, Ellexia Leonie Hanif Assalmi, Fityan Herdianti, Rahmalia Anindya I Gede Susrama Mas Diyasa I Gede Susrama Mas Diyasa idhom, Mohammad Indira Iqbal Ramadhan Anniswa Kartika Maulida Hindrayani Kemal Fahreza Jibran Jibran Made Hanindia Prami Swari Mahardhika, Rivaldi Dwi Maulana, Hendra Maulidya Prastita Syah Maurisa Arimbi Putri Meisya Vira Amelia Mohammad Idhom Mohammad Idhom Muhaimin, Amri Muhammad Hakam Fardana Naomi Dwi Anggraini Nashrullah, Ahmad Firqi Ningrum, Lisya Septyo Parlika, Rizky Prismahardi Aji Riyantoko Putra, Chrystia Aji Putri, Milla Akbarany Bakhtiar Renaldy Al Ikhsan Reza Putri Angga Riswanda, Mohammad Nizar Rizkiyah, Selly Rusdiyanto, Nur Rahmat Safira Kusmindasari, Anggun Salsabila, Nada Shindi Shella May Wara Sinthya Putri, Diana Steffany Marcellia Witanto Suprapto, Claudia Millennia Susrama Mas Diyasa, I Gede Taruna Ardianto Tresna Maulana Fahrudin Trimono Trimono Trimono, Trimono Yisti Vita Via Zahrah, Fathima