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All Journal BAREKENG: Jurnal Ilmu Matematika dan Terapan Jurnal Mnemonic Jurnal Data Mining dan Sistem Informasi The Journal of Enhanced Studies in Informatics and Computer Applications International Journal of Computer Science and Information Technology
Thesa Adi Saputra Yusri
Program Studi Matematika, Fakultas Matematika dan Ilmu Pengetahuan Alam, Universitas Negeri Yogyakarta
Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Energy Engineering Mathematics Mechanical Engineering Physics Transportation

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IMPLEMENTATION OF SMARTER AND ORESTE METHODS FOR DETERMINING UNDERDEVELOPED VILLAGES Riska Amalia Praptiwi; Suaidah Suaidah; Rakhmat Dedi Gunawan; Ryo Cahyo Prakoso; Deddy Rudhistiar; Patricia Evericho Mountaines; Thesa Adi Saputra
Jurnal Data Mining dan Sistem Informasi Vol 4, No 2 (2023): AGUSTUS 2023
Publisher : Universitas Teknokrat Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33365/jdmsi.v4i2.3239

Abstract

Villages have less development than cities because villages have bigger problems such as higher poverty rates, lower health, lower human resources, facilities and infrastructure that are more difficult to reach than cities. Therefore we need the concept of sustainable village development. In sustainable development, the aspect of development is not only aimed at present society but also society in the future. Before making the concept of sustainable village development, so that village development in a city/regency/district area is conceptualized evenly, decision support is needed to identify underdeveloped villages. Some indicators villages or underdeveloped regions mostly related to the survey of Potensi Desa activities by BPS from 1980 to 2014 continually participated. Related to that conditions, criteria obtained underdeveloped villages by DPU and indicator data PODES by BPS, it can be applied on Decision Making System. In this research selected case studies are census data from Potensi Desa by BPS in Magetan. This system uses SMARTER (Simple Multi-Attribute Rating Technique Exploiting Ranks) methods as the calculation of the weights to the criteria and ORESTE methods used for the rankings of underdeveloped villages. In this system SMARTER methods using a weighting formula Rank Order Centroid (ROC) that is proportional weighting which reflects the distance and the priority of each criteria appropriately. Furthermore, the ranking process using Oreste methods by three main stages that is Projection Matrix position, Ranking of projections and Agegration of Global Ranking. Testing of this system, which one is changing the parameters of Oreste (α value) and obtained compatibility reach 91.06% of accuracy to the experts data of underdeveloped villages from the BPS Magetan by the number 100% alternative data with value 0:01 of alpha
ENKRIPSI CITRA DIGITAL BERBASIS KOMBINASI ARNOLD CAT MAP TERMODIFIKASI DAN DNA ENCODING Yusri, Thesa Adi Saputra; Rudhistiar, Deddy
Jurnal Mnemonic Vol 5 No 2 (2022): Mnemonic Vol. 5 No. 2
Publisher : Teknik Informatika, Institut Teknologi Nasional malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36040/mnemonic.v5i2.4901

Abstract

This research was developed with the aim of improving the digital image encryption method based on the modified Arnold Cat Map with DNA encoding diffusion. The focus of this research is to analyze the performance of image randomization using a modified Arnold Cat Map that is diffused by DNA encoding. The encryption results were tested with 4 numerical parameters, namely entropy, Mean-squared error (MSE), peak signal-tonoise ratio (PSNR) and correlation coefficient. The test image is devoted to square images with size variations from 128 x 128 to 512 x 512. The results show the significance of changes in the encryption results when viewed from the histogram conditions that are evenly distributed. The average correlation value is 0.017, the average entropy is 7.9964 and the PSNR is 5.487 dB.
KLASIFIKASI VARIETAS KACANG MENGGUNAKAN XGBOOST DENGAN PENYESUAIAN CLASS WEIGHTING Yusri, Thesa Adi Saputra; Rudhistiar, Deddy; Ratnasari, Andika Putri
Jurnal Mnemonic Vol 8 No 1 (2025): Mnemonic Vol. 8 No. 1
Publisher : Teknik Informatika, Institut Teknologi Nasional malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36040/mnemonic.v8i1.12788

Abstract

Kondisi data tidak imbang (imbalance) merupakan salah satu tantangan utama dalam masalah klasifikasi terkait kualitas atau penyakit pada bidang agrikultur. Penelitian ini bertujuan untuk mengimplementasikan algoritma XGBoost dalam klasifikasi varietas kacang kering dengan fokus pada penanganan ketidakseimbangan kelas melalui pembobotan kelas. Dataset yang digunakan terdiri dari tujuh jenis kacang kering dengan berbagai karakteristik fisik yang diukur dalam piksel, yang meliputi fitur dimensi dan bentuk. Proses normalisasi dilakukan menggunakan teknik min-max normalization untuk memastikan skala data konsisten. Untuk menangani ketidakseimbangan kelas, teknik pembobotan kelas diterapkan dalam XGBoost, yang memberikan bobot lebih pada kelas minoritas. Grid Search dengan 5-fold cross-validation digunakan untuk menemukan kombinasi hyperparameter terbaik, yang menghasilkan akurasi cross-validation sebesar 92.5% dan skor terbaik pada 92.8%. Evaluasi model pada data uji menunjukkan akurasi 93%, dengan hasil precision, recall, dan f1-score yang seimbang pada setiap kelas. Hasil ini menunjukkan bahwa XGBoost dengan pembobotan kelas dapat mengatasi ketidakseimbangan kelas dan memberikan akurasi yang tinggi pada klasifikasi kacang kering
Design and Implementation of a Construction Budgeting Application for Residential Projects on Android Platform Using the Waterfall Method Deddy Rudhistiar; Muhammad Hasan Wahyudi; Hadi Surya Wibawanto Sunarwadi; Amar Rizqi Afdholy; Thesa Adi Saputra Yusri
Journal of Enhanced Studies in Informatics and Computer Applications Vol. 2 No. 1 (2025): JESICA Vol. 2 No. 1 2025
Publisher : Institut Teknologi, Sains, dan Kesehatan RS.DR. Soepraoen Kesdam V/BRW

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47794/jesica.v2i1.26

Abstract

Building a home is one of the most important and basic human needs. However, uncertainty around construction schedules and cost details often pose significant obstacles for individuals and families. The process can be overwhelming, especially for those with less experience in building homes. This is where information technology can play a vital role in streamlining the home-building process. By harnessing the power of technology, the building process can be simplified, providing greater transparency and convenience to users. To address these challenges, the Halo Rumah app was developed. The app helps users choose the right design and materials for their home construction. The app has an intuitive interface that allows users to explore a variety of home designs tailored to different preferences and choose materials that suit their needs and budget. Additionally, the app offers a range of advanced tools that help users estimate and calculate the costs of building a home, giving them a clear picture of their financial needs before starting the building process. By integrating these features, the Halo Rumah app ensures that users can make informed decisions, reduce uncertainty, and plan their dream home more effectively. Based on black box testing of 5 features tested on the Halo Rumah application, the research results show that the 5 features in the application can function according to the expected results.
STOCK PRICE PREDICTION FOR BANK SYARIAH INDONESIA USING BIDIRECTIONAL LONG SHORT-TERM MEMORY (BI-LSTM) Deddy Rudhistiar; Muhammad Hasan Wahyudi; Widhy Wahyani; Thesa Adi Saputra Yusri
International Journal of Computer Science and Information Technology Vol. 1 No. 2 (2024): IJCOMIT Vol 1 No 2
Publisher : Computer Science Department, Malang National Institute of Technology

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36040/ijcomit.v1i2.12320

Abstract

The evolution of global financial markets has heightened the demand for accurate stock price prediction methods, particularly in the Islamic banking sector, which operates under unique principles of Sharia compliance. This study aims to predict the stock prices of Bank Syariah Indonesia (BSI) using a Bidirectional Long Short-Term Memory (Bi-LSTM) model. The dataset comprises daily closing prices from January 2022 to June 2024. The model is optimized through systematic hyperparameter tuning, including configurations for the number of layers, neurons, batch size, learning rate, and optimizers. Evaluation using Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE) identifies the Adam optimizer with a learning rate of 0.001 and batch size of 16 as the optimal configuration. The results highlight that while increasing the number of neurons or layers reduces minimum error, it increases model instability. This research provides novel insights into the application of Bi-LSTM for predicting Islamic banking stock prices, supporting data-driven decision-making in the Islamic financial sector.
DEVELOPMENT OF A WEB-BASED REGISTRATION SYSTEM AT THE TUNJUNGSEKAR SUBDISTRICT OFFICE Deddy Rudhistiar; Muhammad Hasan Wahyudi; Cornelia Luba Tara Boro; Siti Mutiara; Thesa Adi Saputra Yusri
International Journal of Computer Science and Information Technology Vol. 2 No. 1 (2025): IJCOMIT Vol 2 No 1
Publisher : Computer Science Department, Malang National Institute of Technology

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36040/ijcomit.v2i1.14171

Abstract

The Tunjungsekar subdistrict office regularly organizes various activities involving local participants. Currently, the registration process is conducted using Google Forms, which, while facilitating data collection, presents challenges such as duplicate registrations and difficulties in managing participant quotas. Additionally, participant data processing and activity summaries are still handled manually, reducing administrative efficiency. To address these issues, a web-based registration system was developed using PHP with the Laravel framework and a MySQL database. This system enables automatic validation to prevent duplicate registrations and allows for more effective participant quota management. Blackbox taesting shows that the system works, the system functions properly without significant bugs or errors. With this system, activity data management becomes more structured, participant records are more accurate, and administrative processes are more efficient. The implementation of this system is expected to support the digitalization of community services and enhance the community’s experience in accessing information and services more practically and systematically
A COMPARATIVE ANALYSIS OF DBSCAN AND GAUSSIAN MIXTURE MODEL FOR CLUSTERING INDONESIAN PROVINCES BASED ON SOCIOECONOMIC WELFARE INDICATORS Andayani, Sri; Retnani, Namita; Yusri, Thesa Adi Saputra; Marwoto, Bambang Sumarno Hadi
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 3 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss3pp2039-2056

Abstract

Public welfare refers to a condition in which people experience happiness, comfort, prosperity, and can adequately fulfill their basic needs. Indonesia consists of several provinces, each with varying levels of welfare. One crucial aspect in promoting equitable development is ensuring that all regions in Indonesia achieve similar welfare standards. This study aims to classify Indonesian provinces based on socioeconomic welfare indicators, with the results serving as a basis for policy-making that considers regional potential and challenges. The data used in this study are secondary data obtained from the official website of BPS-Statistics Indonesia on provincial welfare indicators from 2020 to 2023. The research methodology includes data collection, descriptive statistical analysis, determining the optimal number of clusters, and comparing the clustering performance of Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and the Gaussian Mixture Model (GMM) using Silhouette Index, Davies-Bouldin Index, and Calinski-Harabasz Index as evaluation metrics. The DBSCAN-based clustering resulted in two clusters: high-welfare and low-welfare regions. Meanwhile, GMM clustering produced five clusters: moderate, fairly low, low, high, and fairly high welfare regions. Based on cluster validity measures, GMM outperformed DBSCAN, achieving a Silhouette score of 0.28, a Davies-Bouldin Index of 1.12, and a Calinski-Harabasz Index of 10.9.
Co-Authors Amar Rizqi Afdholy Cornelia Luba Tara Boro Deddy Rudhistiar Gunawan, Rakhmat Dedi Hadi Surya Wibawanto Sunarwadi Marwoto, Bambang Sumarno Hadi Muhammad Hasan Wahyudi Patricia Evericho Mountaines Ratnasari, Andika Putri Retnani, Namita Riska Amalia Praptiwi Ryo Cahyo Prakoso Siti Mutiara Sri Andayani suaidah suaidah Widhy Wahyani