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All Journal Jurnal Teknologi Informasi dan Ilmu Komputer IJEBD (International Journal Of Entrepreneurship And Business Development) Journal of Information Systems and Informatics 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) International Journal of Robotics and Control Systems 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.
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
Fuzzy Time Series Cheng Optimasi Adaptive Particle Swarm Optimization (APSO) untuk Optimalisasi Prediksi Harga Beras di Kota Surabaya Ulayya, Yasmin; Idhom, Mohammad; Diyasa, I Gede Susrama Mas
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 13 No 1: Februari 2026
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2026131

Abstract

Harga beras rentan mengalami fluktuasi, berdampak signifikan pada kesejahteraan masyarakat, terutama kelompok berpendapatan rendah. Di Surabaya, kenaikan harga beras mendorong perlunya prediksi akurat untuk mitigasi dampak ekonomi. Penelitian ini bertujuan meramalkan harga beras menggunakan metode Fuzzy Time Series Cheng (FTS Cheng) yang dioptimalkan dengan Adaptive Particle Swarm Optimization (APSO) untuk menangani data non-linear dan fluktuatif. Data sekunder diambil dari Dinas Perindustrian dan Perdagangan Provinsi Jawa Timur (Siskaperbapo) periode 1 Januari 2023 hingga 31 Maret 2025, mencakup harga beras premium dan medium di Pasar Tambahrejo dan Pasar Wonokromo. Metode utama adalah FTS Cheng dengan optimasi APSO untuk meningkatkan akurasi prediksi. Model menunjukkan akurasi tinggi dengan MAPE (Mean Absolute Percentage Error) sangat rendah. Di Pasar Tambahrejo, MAPE beras premium 0,09% dan medium 0,00%. Di Pasar Wonokromo, MAPE premium 6,38% dan medium 0,85%. Optimasi APSO berhasil menurunkan MAPE, misalnya di Pasar Tambahrejo (premium turun 0,38%, medium turun 0,68%). Kombinasi FTS dan APSO menghasilkan prediksi harga beras yang presisi. Temuan ini dapat mendukung kebijakan stabilisasi harga, manajemen stok, dan perencanaan produksi beras lebih efektif, sekaligus meningkatkan stabilitas ekonomi rumah tangga.   Abstract Rice prices are prone to fluctuations, significantly impacting public welfare, especially low-income groups. In Surabaya, rising rice prices necessitate accurate predictions to mitigate economic impacts. This research aims to forecast rice prices using the Fuzzy Time Series Cheng (FTS Cheng) method optimized with Adaptive Particle Swarm Optimization (APSO) to handle non-linear and fluctuating data. Secondary data was obtained from the East Java Provincial Department of Industry and Trade (Siskaperbapo) for the period January 1, 2023, to March 31, 2025, covering premium and medium rice prices at Tambahrejo Market and Wonokromo Market. The main method is FTS Cheng with APSO optimization to improve prediction accuracy. The model demonstrates high accuracy with very low MAPE (Mean Absolute Percentage Error). At Tambahrejo Market, MAPE for premium rice is 0.09% and medium rice is 0.00%. At Wonokromo Market, MAPE for premium rice is 6.38% and medium rice is 0.85%. APSO optimization successfully reduces MAPE, for example at Tambahrejo Market (premium decreased by 0.38%, medium decreased by 0.68%). The combination of FTS Cheng and APSO produces precise rice price predictions. These findings can support price stabilization policies, stock management, and more effective rice production planning, while improving household economic stability.
Analysis Postponed VAT Feature on Invoicing Module of Odoo 16 using Rapid Application Development Permana, Eriko Indra; Diyasa, I Gede Susrama Mas; Swari, Made Hanindia Prami
EDUTIC Vol 12, No 1: 2025
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/edutic.v12i1.28484

Abstract

The postponement of Value Added Tax (VAT) payment is a policy aimed at easing financial burdens for companies that frequently import goods, as it allows businesses to defer tax payments instead of prepaying them during imports, thereby improving cash flow and reducing operational costs. This study explores the implementation of VAT payment postponement in the Odoo 16 Invoicing module using the Rapid Application Development (RAD) method, chosen for its rapid iteration and prototyping capabilities to meet user needs and regulatory changes efficiently. By modeling an importing company’s business process in Odoo 16, the research implements and tests the VAT postponement feature, assessing its effectiveness in streamlining operations and enhancing financial flexibility. The study also evaluates the RAD method's efficiency in development and deployment, providing insights into the integration of fiscal policies with corporate IT systems to bolster operational performance and global competitiveness.
Co-Authors A, Denisa Septalian A. A. Ngurah Gunawan Adila, Ma’ratul Agussalim, Agussalim Ali, Munawar Awang, Wan Suryani Wan Bambang Trigunarsyah, Bambang Basuki Rahmat Masdi Siduppa Dwi Arman Prasetya Erma Suryani Eva Yulia Puspaningrum Fahrudin, Tresna Maulana Faikul Umam Fetty Tri Anggraeny Humairah, Sayyidah I Made Merdana Ibrahim, Mohd Zamri Bin idhom, Mohammad Karim, Mohammad Daniel Sulthonul Kartika Maulida Hindrayani Kusuma, Nugraha Varrel Kuswardana, Dendy Arizki Made Hanindia Prami Swari Mudjahidin NYOMAN DITA PAHANG PUTRA, NYOMAN P, Made Hanindia Permana, Eriko Indra Prayoga, Aji Prismahardi Aji Riyantoko Putra, Handie Pramana Sampurno, Ilham Ade Widya Sibarani, Michael Alexander Justin Audison Sugiarto S Trimono, Trimono Ulayya, Yasmin Viet Ha, Nguyen Wahyu Syaifullah Jauharis Saputra Yisti Vita Via Zahrah, Fathima Zamri, Mohd