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All Journal Jurnal Teknologi Informasi dan Ilmu Komputer Register: Jurnal Ilmiah Teknologi Sistem Informasi IJEBD (International Journal Of Entrepreneurship And Business Development) Informatik : Jurnal Ilmu Komputer Journal of Information Systems and Informatics bit-Tech International Journal of Advances in Data and Information Systems Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika Nusantara Science and Technology Proceedings Jurnal Teknik Informatika (JUTIF) Journal of Applied Data Sciences International Journal of Robotics and Control Systems Jurnal Impresi Indonesia Literasi Nusantara Journal of International Conference Proceedings Jurnal Ilmiah Edutic : Pendidikan dan Informatika Signal and Image Processing Letters
Diyasa, I Gede Susrama Mas
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Customer Transaction Clustering with K-Prototype Algorithm Using Euclidean-Hamming Distance and Elbow Method Kuswardana, Dendy Arizki; Prasetya, Dwi Arman; Trimono, Trimono; Diyasa, I Gede Susrama Mas; Awang, Wan Suryani Wan
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.v6i2.1381

Abstract

This study aims to cluster customer transactions in a Japanese food stall using the K-Prototype Algorithm with a combination of Euclidean-Hamming Distance and the Elbow method. Facing intense industry competition, this study seeks to understand customer purchasing behavior to increase loyalty and sales. From 9.721 initial entries, 9.705 cleaned and transformed records were analyzed. K-Prototype was chosen because of its ability to handle numeric features (Total Sales, Product Quantity) and categorical features (Payment Method, Order Type, Day Category and Time Category). The combination of Euclidean-Hamming distances was used for distance measurement. The optimal number of clusters was determined using the Elbow method, with the results recommending three clusters as the most optimal number. A Silhouette score of 0.6191 indicates a Good Structure clustering result, effectively identifying three distinct customer grouping: "Loyal Regulars" (49.5%), "Casual Shoppers" (42.3%), and "Premium Shoppers" (8.2%). Statistical validity was also tested using ANOVA and Chi-Square, the results showed significant differences between the clusters in numerical and categorical variables with a p-value <0.0001. The clusters are statistically valid in both numerical and categorical aspects. These insights provide an understanding of customer characteristics and reveal a strategically valuable cluster for targeted marketing.
Pendekatan Time Series Decomposition (STL) Dalam Prediksi Kecelakaan Berbasis Kepadatan Lalu Lintas Sebagai Dasar Kebijakan Di Tol Surabaya-Gempol Rizky Mahendra, Rakha; Damaliana, Aviolla Terza; Diyasa, I Gede Susrama Mas
Jurnal Impresi Indonesia Vol. 4 No. 5 (2025): Jurnal Impresi Indonesia
Publisher : Riviera Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58344/jii.v4i5.6491

Abstract

Kecelakaan lalu lintas di jalan tol tetap menjadi masalah kritis yang mempengaruhi keselamatan publik dan stabilitasekonomi. Penelitian ini mengusulkan penggunaan dekomposisi Seasonal-Trend menggunakan LOESS (STL) untukmemprediksi risiko kecelakaan berdasarkan data volume lalu lintas di jalan tol Surabaya-Gempol. Data dari Januari 2022hingga Desember 2023, termasuk volume lalu lintas harian dan laporan kecelakaan, diuraikan menjadi komponen tren,musiman, dan residu untuk mengidentifikasi pola. Korelasi positif sedang (r = 0,4882) ditemukan antara volume lalulintas dan frekuensi kecelakaan. Analisis STL mengungkapkan puncak musiman mingguan yang konsisten di akhir pekan,terutama hari Sabtu. Model prediktif yang dikembangkan berhasil mengidentifikasi 11 hari berisiko tinggi pada Januari2024. Berdasarkan temuan tersebut, delapan rekomendasi kebijakan berbasis waktu dirumuskan, termasuk manajemenlalu lintas dinamis, pemantauan real-time, dan peningkatan pengawasan selama periode puncak. Penelitian ini menyumbangkan kerangka kerja berbasis data baru untuk manajemen keselamatan lalu lintas, menggabungkandekomposisi deret waktu dengan panduan kebijakan yang dapat ditindaklanjuti. Tidak seperti penelitian sebelumnya yanghanya berfokus pada prediksi volume, atau pada konteks jalan non-tol, penelitian ini memajukan penerapan STL untukidentifikasi risiko real-time di jalan tol Indonesia. Implikasinya menekankan integrasi sistem lalu lintas cerdas dan potensiprakiraan berbasis STL sebagai fondasi strategi keselamatan jalan nasional.
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.
Utility-Based Buffer Management for Enhancing DTN Emergency Alert Dissemination in Jakarta's Urban Rail Systems Agussalim, Agussalim; Viet Ha, Nguyen; Putra, Handie Pramana; Adila, Ma’ratul; Diyasa, I Gede Susrama Mas; Rahmat, Basuki
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 4 (2025): JUTIF Volume 6, Number 4, Agustus 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.4.5241

Abstract

The efficiency of emergency alert dissemination in highly populated and densely urban transport networks, such as Jakarta's integrated rail system, is undermined by sporadic connectivity and limited network resources. In this environment, an initial comparison of baseline Delay-Tolerant Network (DTN) routing protocols revealed that flooding-based routers, such as Epidemic, while achieving above-average delivery rates, suffered from high overhead and poor buffer utilization. This paper fills this gap by proposing the Combined Utility Router, a novel buffer management policy that overcomes the limitations of naive strategies, such as Drop-Oldest. Our approach holistically evaluates a message's value by assigning a weighted utility function based on its Time-To-Live (TTL), estimated total replicas, message size, and a user-defined priority. The router maintains high-value messages by discarding the message deemed the lowest utility score under the buffer constraint. Utility-based simulations in The ONE simulator demonstrate that applying our approach to Epidemic routing improves delivery probability, reduces average latency in high network congestion scenarios, while maintaining overhead rates. This work confirms that, in the context of developing reliable and efficient emergency communication systems for challenging urban topographies, optimizing buffer management extends beyond simply selecting the appropriate protocol.
Daily Forecasting for Antam's Certified Gold Bullion Prices in 2018-2020 using Polynomial Regression and Double Exponential Smoothing Fahrudin, Tresna Maulana; Riyantoko, Prismahardi Aji; Hindrayani, Kartika Maulida; Diyasa, I Gede Susrama Mas
Journal of International Conference Proceedings Vol 3, No 4 (2020): Proceedings of the 8th International Conference of Project Management (ICPM) Mal
Publisher : AIBPM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32535/jicp.v3i4.1009

Abstract

Gold investment is currently a trend in society, especially the millennial generation. Gold investment for the younger generation is an advantage for the future. Gold bullion is often used as a promising investment, on other hand, the digital gold is available which it is stored online on the gold trading platform. However, any investment certainly has risks, and the price of gold bullion fluctuates from day to day. People who invest in gold hopes to benefit from the initial purchase price even if they must wait up to five years. The problem is how they can notice the best time to sell and buy gold. Therefore, this research proposes a forecasting approach based on time series data and the selling of gold bullion prices per gram in Indonesia. The experiment reported that Holt’s double exponential smoothing provided better forecasting performance than polynomial regression. Holt’s double exponential smoothing reached the minimum of Mean Absolute Percentage Error (MAPE) 0.056% in the training set, 0.047% in one-step testing, and 0.898% in multi-step testing.
Comparison of Elbow and Silhouette Methods in Optimizing K-Prototype Clustering for Customer Transactions Kuswardana, Dendy Arizki; Prasetya, Dwi Arman; Trimono, Trimono; Diyasa, I Gede Susrama Mas
EDUTIC Vol 12, No 1: 2025
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/edutic.v12i1.29744

Abstract

This research presents a comparative analysis of the Elbow and Silhouette methods to identify the ideal number of clusters in applying the K-Prototypes algorithm for customer grouping using purchase transaction data. The K-Prototypes algorithm is employed due to its ability to handle both numerical and categorical data simultaneously. Customer purchase transaction data from the Point of Sale (POS) system is analyzed through preprocessing, feature transformation, and attribute segmentation stages before being clustered using the K-Prototypes algorithm. To identify the optimal cluster count, this study employs two methods: the Elbow and the Silhouette method. The results indicate that the Elbow method produces 2 clusters with a model evaluation score of 0.6368, while the Silhouette method suggests 2 clusters with a slightly lower score of 0.6186. In terms of computational efficiency, the Elbow method also demonstrates a faster processing time results highlight the significance of choosing an appropriate method for identifying the ideal number of clusters, ensuring it aligns with the specific goals of the analysis, whether emphasizing superior inter-cluster distinction or favoring a more parsimonious model configuration.
Implementation Of Natural Language Processing for Spam Email Detection in Outcome Based Education (OBE) Application Diyasa, I Gede Susrama Mas
IJEBD (International Journal of Entrepreneurship and Business Development) Vol 6 No 6 (2023): November 2023
Publisher : LPPM of NAROTAMA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29138/ijebd.v6i6.2587

Abstract

The Natural Language Processing (NLP) approach has been proven to be effective in spam detection in e-mail because of its ability to process text and identify patterns and distinctive characteristics of spam e-mail. Methods in this NLP approach include data pre-processing, such as removing punctuation, irrelevant common words, tokenization, stemming, and others, as well as classification techniques such as Support Vector Classifier (SVC), Naive Bayes, and others. In testing various models, there is one model that shows the highest precision with the number 0.98. This study shows that the NLP approach provides better performance in spam detection compared to other methods. However, it is necessary to improve technology and develop more complex detection methods to improve the performance and accuracy of the email spam detection model
Implementasi K-Means Clustering dengan Menggunakan Data Transaksi Penjualan untuk Penentuan Reward pada Agen Aqua dan Gas LPG FF Tirta Sari, Refika Ayuna; Seta, Henki Bayu; Diyasa, I Gede Susrama Mas
Informatik : Jurnal Ilmu Komputer Vol 18 No 3 (2022): Desember 2022
Publisher : Fakultas Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52958/iftk.v18i3.4673

Abstract

Target pelanggan bagi Agen Aqua dan Gas LPG sangatlah penting karena persaingan antar perusahaan sejenis mungkin terjadi dan mengakibatkan hilangnya kepuasan pelanggan. Penelitian ini memiliki tujuan yaitu membangun sistem yang dapat mengelompokkan dan mengetahui tingkat target pelanggan berdasarkan transaksi pembelian agar pihak agen dapat mempertahankan pelanggan yang potensial tersebut dengan cara memberikan memberikan hadiah berupa promo spesial (reward). Hasil penelitian ini ada pengelompokan pelanggan yang mendapatkan reward dan tidak dapat dilihat perbulannya, yaitu pada bulan Januari ada 75 pelanggan mendapatkan reward dan 100 tidak mendapatkan reward, Februari ada 70 pelanggan mendapatkan reward dan 106 tidak mendapatkan reward, Maret ada 80 pelanggan mendapatkan reward dan 96 tidak mendapatkan reward, April ada 35 pelanggan mendapatkan reward dan 141 tidak mendapatkan reward, Mei ada 65 pelanggan yang mendapatkan reward dan 111 tidak yang mendapatkan reward, Juni ada 43 pelanggan yang mendapatkan reward dan 133 tidak mendapatkan reward l, Juli ada 77 pelanggan yang mendapatkan reward dan 99 tidak mendapatkan reward, Agustus ada 48 pelanggan yang mendapatkan reward dan 128 tidak mendapatkan reward, September ada 94 pelanggan mendapatkan reward dan 82 tidak mendapatkan reward, dan Oktober ada 94 pelanggan mendapatkan reward dan 82 tidak mendapatkan reward.Kata Kunci: Clustering, K-Means Clustering, Reward, Davies-Bouldin Index
Data Augmentation of Sperm Images Using Generative Adversarial Networks (WGAN-GP) Diyasa, I Gede Susrama Mas; Kuswardhani , Hajjar Ayu Cahyani; Idhom, Mohammad; Riyantoko, Prismahardi Aji; Dewi , Deshinta Arrova
Register: Jurnal Ilmiah Teknologi Sistem Informasi Vol 12 No 1 (2026): January (In Progress)
Publisher : Information Systems - Universitas Pesantren Tinggi Darul Ulum

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26594/register.v12i1.5954

Abstract

This study analyzes the use of WGAN-GP for data augmentation in the analysis of sperm morphology. WGAN-GP has been the focus in this study for generating sperm microscopy images, which in turn aims to mitigate the problem of data scarcity in medical imaging. A heterogeneous dataset with mixed object categories was initially employed, leading to an FID score of 134, which in turn reflected a high incidence of mode collapse. For this reason, the dataset was divided into subcategories of Normal, Abnormal, and Non-Sperm identifications, with the scores of the subcategories being 59.19, 74.92, and 83.56, respectively, and showing better balanced model stability. This study's primary contribution is the use of WGAN-GP for the first time for sperm image data augmentation and the generation of more realistic synthetic images. Furthermore, this study illustrates the first understanding of the intricacies of data distribution's complexity and its effect on the model's performance, indicating the possibility of improvement using class-based techniques and sophisticated architectures for the generator. The innovation of this study is the application of WGAN-GP to sperm morphology datasets, improving image quality and the stability of the results, coupled with extensive model performance analysis and providing a further understanding of the field of medical image data augmentation.
Face Detection Based on Anti-Spoofing with FaceNet Method for Filtering Contract Cheating in Online Exam Ujianto, Erik Iman Heri; Diyasa, I Gede Susrama Mas; Junaidi, Achmad; Fatullah, Ryan Reynickha; Permanasari, Wahyu Melinda; Sari, Allan Ruhui Fatmah
Journal of Applied Data Sciences Vol 7, No 1: January 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i1.1167

Abstract

This study develops a reliable face-based verification system for online examinations by integrating a face recognition model with a blink detection mechanism to minimize the risk of identity fraud, also known as "contract cheating," and static image manipulation. "Contract cheating" refers to the practice where students hire others to complete their exams or assignments, compromising academic integrity. The growing reliance on online exams has raised concerns about the credibility of facial verification, as conventional methods are often vulnerable to spoofing attempts. To address this issue, the proposed system combines FaceNet, a deep learning model for identity recognition, with Dlib’s eye blink detection to provide a stronger layer of protection. The system was evaluated using 5-fold and 10-fold K-fold cross-validation, and additional testing assessed the impact of different video frame rates on performance. The results show that the system performs effectively in identifying legitimate users and detecting spoofing. FaceNet achieved an accuracy of 96.67 percent, outperforming DeepFace, which showed poorer results in precision, recall, and F1 score for some participants. Both models were evaluated on the same dataset, consisting of 150 images. The preprocessing pipeline, including face detection using MTCNN, cropping, and resizing, was applied consistently to both models to ensure a fair comparison of their performance. The system also demonstrated adaptability, achieving correct classifications at both 15 and 30 frames per second. Anti-spoofing tests based on the eye blink detection system detected all real faces, while static images were classified as spoofing. These results confirm that combining face recognition with liveness detection enhances the security of online examination platforms. The findings demonstrate the system's potential to reduce contract cheating and impersonation fraud, making online examinations more credible. Future work may focus on implementing adaptive thresholding for blink detection and integrating multimodal verification techniques to improve robustness across diverse real-world environments.
Co-Authors A, Denisa Septalian A. A. Ngurah Gunawan Achmad Junaidi Adila, Ma’ratul Agussalim, Agussalim Ali, Munawar Awang, Wan Suryani Wan Bambang Trigunarsyah, Bambang Basuki Rahmat Masdi Siduppa Damaliana, Aviolla Terza Dewi , Deshinta Arrova Dwi Arman Prasetya Erik Iman Heri Ujianto Erma Suryani Eva Yulia Puspaningrum Fahrudin, Tresna Maulana Faikul Umam Fatullah, Ryan Reynickha Fetty Tri Anggraeny Henki Bayu Seta Humairah, Sayyidah I Made Merdana Ibrahim, Mohd Zamri Bin idhom, Mohammad Karim, Mohammad Daniel Sulthonul Kartika Maulida Hindrayani Kusuma, Nugraha Varrel Kuswardana, Dendy Arizki Kuswardhani , Hajjar Ayu Cahyani Made Hanindia Prami Swari Moerrin, Naufal Baihaqi Mudjahidin NYOMAN DITA PAHANG PUTRA, NYOMAN P, Made Hanindia Permana, Eriko Indra Permanasari, Wahyu Melinda Prayoga, Aji Putra, Handie Pramana Riyantoko, Prismahardi Aji Rizky Mahendra, Rakha Sampurno, Ilham Ade Widya Saputra, Wahyu Syaifullah Jauharis Sari, Allan Ruhui Fatmah Sari, Refika Ayuna Sibarani, Michael Alexander Justin Audison Sugiarto S Trimono, Trimono Ulayya, Yasmin Viet Ha, Nguyen Yisti Vita Via Zahrah, Fathima Zamri, Mohd