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All Journal JURNAL MEDIA INFORMATIKA BUDIDARMA Jurnal Ilmiah Matrik Building of Informatics, Technology and Science J-Intech (Journal of Information and Technology)
Haristyawan, Ivan
STMIK Widya Cipta Dharma
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Library & Information Science

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IMPLEMENTASI FSM SEBAGAI RESPON OTONOM DAN ADAPTIF NPC PADA GAME “AWANG MENJELAJAHI KOTA TENGGARONG” Azahari Azahari; Ivan Haristyawan; Muhammad Jariansyah
Jurnal Ilmiah Matrik Vol 22 No 2 (2020): Jurnal Ilmiah Matrik
Publisher : Direktorat Riset dan Pengabdian Pada Masyarakat (DRPM) Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33557/jurnalmatrik.v22i2.987

Abstract

Development of the game towards a tourist promotion tool is very important. The game "Awang exploring the City of Tenggarong" is an android smartphone game that tells the story of a tourist / traveler named Awang who roams every tourist destination in the Tenggarong City. The development of this game uses multimedia development life cycle, starting from the concept, assembly, and testing. Artificial intelligence is also needed in the development of this game. Players will be accompanied by NPC (Non Player Character) in the form of a female tour guide. NPCs equipped with the Finite State Machine (FSM) model can guide players to go to each Tenggarong tourist attraction, and it can also provide information related to these tourist attractions. The final result of this research is a game that can be a media for tourism promotion in Tenggarong city
Implementasi Data Mining dengan Menerapkan Algoritma K-Means Clustering untuk Memberikan Rekomendasi Jurusan Kuliah Bagi Mahasiswa Baru Arfyanti, Ita; Bustomi, Tommy; Haristyawan, Ivan
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 2 (2024): April 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i2.7429

Abstract

At the tertiary level, a student studies in a field of expertise or major that suits his or her area of talent and interest. Choosing an inappropriate college major will have consequences for the future of the prospective new student. In choosing a major, a prospective new student should choose a major that suits his abilities, both academically and his talents. One way to overcome prospective students who are wrong in choosing this major is to use the K-Means Clustering method. The K-Means algorithm is part of clustering data mining which has the role of forming new groups based on cluster formation. The K-Means Clustering algorithm can solve the problem of recommending majors to prospective new students based on school grades. The results of applying the K-Means algorithm show that in Cluster 1 there are 6 prospective students, in Cluster 2 there are 11 prospective students and in Cluster 3 there are 3 prospective students.
Perbandingan Kinerja Algoritma Klasifikasi Data Mining Untuk Prediksi Penyakit Darah Tinggi Arfyanti, Ita; Bustomi, Tommy; Haristyawan, Ivan
Building of Informatics, Technology and Science (BITS) Vol 6 No 3 (2024): December 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i3.6477

Abstract

High blood pressure or hypertension is one of the major health problems in the world. Although this disease can be treated, many individuals are unaware that they have hypertension, because the symptoms are often not visible or felt. Therefore, early detection of high blood pressure is very important to prevent serious complications that can endanger health. In the digital era and advances in information technology, a lot of health data can be used for analysis. One of the rapidly developing approaches to help diagnose disease is by utilizing data mining. Data mining is the process of exploring and analyzing big data to find hidden patterns, information, and knowledge that can be used to support decision making and predictions. One technique in data mining that is often used to predict conditions or diseases is the classification algorithm. However, the comparison of performance between these classification algorithms in the context of hypertension prediction is still limited. This study aims to explore and compare the performance of classification algorithms in predicting hypertension, using a dataset containing medical information about factors that affect a person's blood pressure. The Naive Bayes algorithm is a classification method based on Bayes' theorem and the assumption of independence between features. The C4.5 algorithm is a machine learning algorithm for building decision trees used in data classification. The results of this study are expected to contribute to the development of a data mining-based decision support system that can be used to detect and predict the risk of hypertension. the accuracy value of the Naive Bayes algorithm is 87.01% and the accuracy value of the C4.5 algorithm is 94.72%. From the process that has been carried out, it can be said that the C4.5 algorithm is an algorithm with better performance than the Naive Bayes algorithm. Thus, the model used in the process of diagnosing hypertension is the model of the C4.5 algorithm.
Application of K-Nearest Neighbor Algorithm For Sentiment Analysis On Free Fire Online Game Based On Google Play Store Reviews Raya, Dimas; Yusnita, Amelia; Haristyawan, Ivan
J-INTECH ( Journal of Information and Technology) Vol 13 No 01 (2025): J-Intech : Journal of Information and Technology
Publisher : LPPM STIKI MALANG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/j-intech.v13i01.1882

Abstract

The swift expansion of the digital gaming sector, especially online games like Free Fire, has produced extensive user feedback via platforms like the Google Play Store. This research utilizes the K-Nearest Neighbor (KNN) algorithm to conduct sentiment analysis on 5,000 user reviews, with the goal of assessing its classification effectiveness. Following preprocessing (case folding, Text Cleaning, tokenization, stopword Removal, stemming), the data was converted using TF-IDF and balanced through SMOTE. Experimental findings indicate that KNN attained a peak accuracy of merely 36.53% (at k = 14), reflecting weak performance with high-dimensional textual data. In contrast, Logistic Regression attained a notably higher accuracy of 88%, showcasing its dominance for this task. The results offer perspectives for game developers to assess user feelings and emphasize the significance of selecting suitable machine learning models. Future research should investigate advanced classifiers like SVM, Random Forest, or deep learning methods to enhance accuracy.
Market Potential Analysis Based on Population and Land Area using K-Means Clustering and MCDM Approaches Arfyanti, Ita; Bustomi, Tommy; Haristyawan, Ivan
Building of Informatics, Technology and Science (BITS) Vol 7 No 1 (2025): June (2025)
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i1.7392

Abstract

In an increasingly competitive global market, accurately identifying untapped market potential in small to medium-sized regions, often overlooked by traditional single-indicator analyses, presents a significant challenge for strategic decision-making. This study addresses this by proposing a hybrid analytical framework integrating K-Means Clustering with Multi-Criteria Decision-Making (MCDM) methods, utilizing population size and land area as core indicators. The primary objective is to develop a robust market potential analysis model capable of systematically classifying regions and providing actionable insights for resource optimization and market expansion. The methodology involves determining the optimal number of clusters using the elbow method (k=3, with a silhouette score of 0.8862), followed by K-Means clustering to segment Asian countries into distinct groups. Subsequently, three MCDM methods SAW, WP, and WASPAS are applied to rank countries within the most relevant cluster (low population and area) under various weighting scenarios. The results consistently demonstrate Turkey's top ranking across all MCDM methods, highlighting its robust market potential regardless of weight variations. Crucially, a very strong agreement in rankings between the MCDM methods was observed, evidenced by Spearman's correlation coefficients consistently above 0.98, with the highest correlation between SAW and WASPAS (0.998379 for [0.3, 0.7] weights). This high correlation confirms the reliability and consistency of the model, concluding that SAW and WASPAS are highly suitable for this analysis, and identifying Turkey as the leading country in market potential among 50 Asian nations based on the criteria studied.
Co-Authors Amelia Yusnita Azahari Bustomi, Tommy Ita Arfyanti Muhammad Jariansyah Raya, Dimas