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All Journal JURNAL MEDIA INFORMATIKA BUDIDARMA Jurnal Ilmiah Matrik METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Building of Informatics, Technology and Science Buletin Poltanesa J-Intech (Journal of Information and Technology) Journal of Informatics, Electrical and Electronics Engineering Management of Information System Journal
Haristyawan, Ivan
STMIK Widya Cipta Dharma
Aerospace Engineering Agriculture, Biological Sciences & Forestry Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education 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.
Penerapan Metode Lean UX Untuk Sistem Poin dan Loyalty Custumer Pada Website Pemesanan Makanan Kitchen Yusuf, Imam Fiqri Haikhal; Yulindawati, Yulindawati; Haristyawan, Ivan
Journal of Informatics, Electrical and Electronics Engineering Vol. 5 No. 2 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/jieee.v5i2.2835

Abstract

The Lean UX method is a design approach that emphasizes collaboration, rapid experimentation, and continuous validation to produce products that meet user needs. On the Kitchen food ordering website, Lean UX was applied to design and develop a points system and customer loyalty system to increase user engagement and retention. The points system was chosen because initial findings from user research indicated that customers wanted clear and sustainable incentives to continue ordering through the Kitchen platform. The Lean UX process began with formulating a hypothesis regarding the benefits of the points system, then continued with the creation of an MVP (Minimum Viable Product) in the form of a reward feature prototype. The prototype was iteratively tested with users through a Build Measure Learn cycle to obtain rapid feedback and identify pain points in the user experience. Through this series of experiments, the team was able to adjust the interface, interaction flow, and reward structure based on real data from user behavior. The results of implementing the Lean UX method showed that iterative testing helped accelerate the feature validation process, reduce development risks, and increase the relevance of the points system to user needs. Thus, Lean UX proved effective in creating loyalty features that not only improve the user experience, but also support Kitchen's business goals of retaining customers and increasing order frequency. to design and develop a points system and customer loyalty as an effort to increase user engagement and retention. The points system was chosen because initial findings from user research showed that customers wanted clear and sustainable incentives to continue ordering through the Kitchen platform. The Lean UX process began with the formulation of a hypothesis regarding the benefits of the points system, then continued with the creation of an MVP (Minimum Viable Product) in the form of a reward feature prototype. The prototype was tested with users iteratively through the Build–Measure–Learn cycle to obtain rapid feedback and identify pain points in the user experience. Through this series of experiments, the team was able to adjust the interface, interaction flow, and reward structure based on real data from user behavior. The results of implementing Lean UX methods showed that iterative testing helped accelerate the feature validation process, reduce development risk, and increase the relevance of the points system to user needs. Thus, Lean UX proved effective in creating a loyalty feature that not only improved the user experience but also supported Kitchen's business goals of retaining customers and increasing order frequency.
Analisis Sentimen Pelanggan Kopi Kenangan pada Media Sosial Instagram Menggunakan Metode Lexicon Based Riani Sela; Wahyuni; Ivan Haristyawan
METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Vol. 10 No. 1 (2026): METHOMIKA: Jurnal Manajemen Informatika & Komputersisasi Akuntansi
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/jmika.Vol10No1.pp198-207

Abstract

This study aims to analyze customer sentiment towards Kopi Kenangan on Instagram using a lexicon-based method. The rapid growth of Kopi Kenangan as a local coffee brand in Indonesia has made Instagram a key platform for building brand image and interacting with customers. The main problem faced is the large volume of unstructured comment data that cannot be processed manually, so an efficient and systematic automated approach is needed. This study uses a case study methodology and quantitative descriptive techniques. The object of research in this study is the official Kopi Kenangan Instagram account, and the dataset used to conduct this study comes from that account. The dataset that can be used to test this research will be created by developing processing stages. The Lexicon-Based method is used in this research approach. The purpose of the dictionary-based Lexicon-Based method is to determine the weight of sentences in the dataset to identify sentiment class labels. The data used in this study comes from 1689 records that have been preprocessed to produce 1438 patent records by removing empty comment records and verifying duplicates up to a certain threshold. The next step is to identify comments based on their sentiment: 25 negative, 1347 neutral, and 317 positive. The results show that negative sentiment reached 1,5%, neutral 79.8%, and positive 18.8%. Based on this presentation, the majority of the text examined was consumer responses to Kopi Kenangan's Instagram posts, and most of them were negative. This indicates that the Lexicon-Based model developed is capable of classifying sentiment with good accuracy.
Implementation of Text Mining for Service Quality Classification of Google Maps Reviews at Samarinda Civil Registry Office Using K-Nearest Neighbor Algorithm Abed Nego; Heny Pratiwi; Ivan Haristyawan
Poltanesa Vol 27 No 1 (2026): June 2026
Publisher : P3KM Politeknik Pertanian Negeri Samarinda

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51967/tanesa.v27i1.3728

Abstract

This research aims to analyze public sentiment regarding services at the Department of Population and Civil Registration as an objective evaluation instrument for service quality through a series of methodological stages, beginning with text preprocessing including cleansing, folding, tokenizing, filtering, and stemming to reduce noise in the scraped textual data. Feature extraction was performed using Term Frequency-Inverse Document Frequency (TF-IDF) to determine the significance of each term within the documents, while the classification process was implemented using the K-Nearest Neighbor (KNN) algorithm by experimenting with various nearest neighbor (k) values to identify the most optimal model parameters. Based on a comprehensive evaluation using a confusion matrix, the model achieved peak performance at k=3 with an accuracy rate of 92.8%, although significant limitations were identified in predicting negative sentiments, only 6 of the 14 data points were correctly classified. This indicates a classification bias triggered by data imbalance, a common challenge in text mining is that the number of positive reviews far outnumbers the number of negative reviews. Qualitatively, although reviews were dominated by public appreciation, critical complaints were still found regarding perceived slow service duration and overlapping bureaucratic complexities. These findings emphasize that while the model possesses high quantitative accuracy, a thorough evaluation of service quality must focus on strategic transformations that improve system efficiency and information transparency to bridge the gap between community expectations and the reality of public service delivery.
Analisis Kepuasan Masyarakat Terhadap Pelayanan E-Ktp di Kantor Kecamatan Kembang Janggut Menggunakan Metode Servqual Nur Syariffah; Ivan Haristyawan; Rizky Zakariyya Rasyad
Management of Information System Journal Vol 4 No 3: Juli 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/mis.v4i3.2863

Abstract

E-KTP service is a form of public service that must be able to meet public expectations. However, various obstacles remain, such as the lengthy service process, lack of officer responsiveness, and technical constraints that have the potential to reduce public satisfaction. This study aims to analyze the level of public satisfaction with E-KTP services at the Kembang Janggut District Office using the SERVQUAL method. The study used a quantitative approach involving 83 respondents selected using the Slovin formula. Data were obtained through distributing questionnaires based on five SERVQUAL dimensions, namely tangibles, reliability, responsiveness, assurance, and empathy, then analyzed using the calculation of the gap value between public perceptions and expectations. The results showed that all dimensions had a negative gap value, which means the quality of service has not fully met public expectations. The responsiveness dimension had the largest gap value of -0.22, making it a top priority in service improvement, while the empathy dimension had the smallest gap value of -0.13, indicating that the attitude and attention of officers have been rated the best by the public. This research provides a contribution in the form of identifying service dimensions that are priority for improvement so that it can be a basis for the Kembang Janggut District Office in developing strategies to improve the quality of E-KTP services and increase public satisfaction.
Analisis Kepuasan Mahasiswa Terhadap Layanan BAAK STMIK WICIDA Menggunakan Metode Servqual Gia Lestari Geaa; Ivan Haristyawan; Rizky Zakariyya Rasyad
Management of Information System Journal Vol 4 No 3: Juli 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/mis.v4i3.3018

Abstract

The Academic and Student Affairs Administration Office (BAAK) plays an important role in supporting academic services and influencing student satisfaction. However, there is still a gap betwìeìen thìe sìervicìes rìecìeivìed by studìents and thìeir ìexpìectations, making it nìecìessary to ìevaluatìe thìe quality of thìe sìervicìes providìed. This study aims to dìetìerminìe thìe lìevìel of studìent satisfaction with thìe sìervicìes of BAAK at STMIK WICIDA using thìe SERVQUAL mìethod. This rìesìearch ìemployìed a quantitativìe approach by distributing quìestionnairìes to studìents. Thìe collìectìed data wìerìe analyzìed by comparing studìents' pìercìeptions and ìexpìectations basìed on thìe fivìe SERVQUAL dimìensions, namìely tangiblìe, rìeliability, rìesponsivìenìess, assurancìe, and ìempathy. Thìe rìesults indicatìe that all sìervicìe dimìensions havìe nìegativìe GAP valuìes, mìeaning that thìe sìervicìes providìed by BAAK havìe not yìet fully mìet studìents' ìexpìectations. Thìe tangiblìe dimìension has thìe largìest GAP valuìe of -0.68, followìed by rìeliability (-0.23), rìesponsivìenìess (-0.16), assurancìe (-0.12), and ìempathy (-0.07). Thìesìe findings providìe an ovìerviìew of thìe sìervicìe aspìects that should bìe prioritizìed for improvìemìent, particularly thìe tangiblìe dimìension, and can sìervìe as ìevaluation matìerial for thìe institution in improving thìe quality of acadìemic and studìent sìervicìes.
Co-Authors Abed Nego Amelia Yusnita Azahari Bustomi, Tommy Gia Lestari Geaa Heny Pratiwi Ita Arfyanti Muhammad Jariansyah Nur Syariffah Raya, Dimas Riani Sela Rizky Zakariyya Rasyad Wahyuni - Wahyuni Yulindawati, Yulindawati Yusuf, Imam Fiqri Haikhal