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All Journal Media Statistika JURNAL SISTEM INFORMASI BISNIS Telematika : Jurnal Informatika dan Teknologi Informasi Seminar Nasional Informatika (SEMNASIF) Jurnal Sistem informasi dan informatika (SIMIKA) International Journal of Advances in Data and Information Systems ESTIMASI: Journal of Statistics and Its Application Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika Nusantara Science and Technology Proceedings Jurnal Teknik Informatika (JUTIF) Journal of Technology and Informatics (JoTI) International Journal Of Computer, Network Security and Information System (IJCONSIST) Journal of International Conference Proceedings Journal of Data Mining and Information Systems Parameter: Jurnal Matematika, Statistika dan Terapannya Joong-Ki
Hindrayani, Kartika Maulida
Sains Data, Universitas Pembangunan Nasional Veteran Jawa Timur
Religion Agriculture, Biological Sciences & Forestry Humanities Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology

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Identifying Academic Excellence: Fuzzy Subtractive Clustering of Student Learning Outcomes Wibowo, Muhammad Bagas Satrio; Hindrayani, Kartika Maulida; Trimono, Trimono
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 3 (2025): JUTIF Volume 6, Number 3, Juni 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.3.4614

Abstract

Education forms a vital foundation for a nation's future. In this digital era, while the use of Information and Communication Technology (ICT) in education is increasing, it brings increasingly complex challenges in education data management and analysis. The growing number of students each year results in a large volume of data, which would be difficult to manage if still relying on manual methods. Manual approaches are inefficient, time-consuming, prone to inconsistencies and human error, especially when identifying outstanding students in large and complex data. This research aims to implement a clustering system to group outstanding students at XYZ elementary school using the Fuzzy Subtractive Clustering (FSC) method. FSC was chosen for its ability to identify data groups based on the density of data points. FSC involves several important parameters, including radius, squash factor, acceptance ratio, and rejection ratio. Added variabel of social and spiritual values aims to enhance grouping quality by offering a broader perspective on students' character, attitudes, and social interactions. Parameter exploration shows an increase in the silhouette score from 0.20–0.45 to 0.45-0.57 and variable addition spiritual and social values, which indicates clearer cluster separation and provides better insights. The best parameters results were achieved with radius 0.3, accept ratio 0.5, reject ratio 0.04, and squash factor 1.25, resulting in a Silhouette Score of 0.57 and forming 5 student groups. Cluster results can guide special mentoring for students with low academic, spiritual, and social values, and support personalized learning programs based on each cluster’s characteristics.
Analisis Sentimen Terhadap Ulasan Aplikasi Mobile JKN Menggunakan Metode Machine Learning Logistic Regression, SVM, dan CSVM Fernando, Moch. Firman; Ahmad, Davin Anezta; Rachmanto, Nugroho Fajar; Wara, Shindi Shella May; Hindrayani, Kartika Maulida
ESTIMASI: Journal of Statistics and Its Application Vol. 6, No. 2, Juli, 2025 : Estimasi
Publisher : Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/ejsa.v6i2.44943

Abstract

One of the digital-based public service innovations in the health sector is the Mobile JKN application developed by BPJS Kesehatan. This application allows people to get health services more easily, effectively, and integrated. The purpose of this study is to evaluate user perceptions of the Mobile JKN application through collecting reviews from the Google Play Store. The collected data was analyzed using TF-IDF text mining technique and Chi-Square feature selection. Furthermore, logistic regression, support vector machine (SVM), and clustered SVM (CSVM) algorithms were used to perform sentiment classification. Comments were categorized into three categories: positive, neutral, and negative. The evaluation results show that CSVM has an accuracy value of 93%, precision of 94%, recall of 84%, and F1 value of 89%. Although features such as online registration and digital cards received positive feedback, sentiment analysis showed that most reviews were negative, especially regarding technical issues. The results show that ML-based algorithms can be effectively used to assess how people perceive digital services. These results can be used as a basis for BPJS Kesehatan to improve and develop new services.
ENHANCED CLUSTERING USING PSO-KMEDOIDS FOR GOVERNMENT AID DISTRIBUTION Fitriani, Aulia Nur; Hindrayani, Kartika Maulida; Trimono, Trimono
Jurnal Sistem Informasi dan Informatika (Simika) Vol. 8 No. 2 (2025): Jurnal Sistem Informasi dan Informatika (Simika)
Publisher : Program Studi Sistem Informasi, Universitas Banten Jaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47080/gegxdv17

Abstract

The distribution of social assistance in Indonesia often experiences problems due to inaccuracies in recipient data between those recorded in government systems and field conditions. In Kalipuro Village, Mojokerto District, data mismatches caused difficulties in screening assistance, requiring village officials to manually re-filter the data. This triggered protests from citizens who should have received assistance but did not get their rights. To overcome this problem, this research proposes the use of the K-Medoids algorithm which is able to overcome sensitivity to outliers. This algorithm is used to cluster data based on criteria such as occupation, number of assets, number of dependents, and income. In addition, this research incorporates the Particle Swarm Optimization (PSO) technique to optimise the clustering process, which is expected to improve accuracy and efficiency in social assistance distribution. The results of clustering analysis using the K-Medoids algorithm show that the best cluster is obtained at the number of clusters K=5, with the distribution of cluster 0 (179 households), cluster 1(89 households), cluster 2 (296 households), cluster 3 (354 households), and cluster 4 (94 households). The Silhouette Score value of 0.6531 indicates good cohesion and separation between clusters. Based on the analysis, cluster 1 is the top priority group of aid recipients, followed by clusters 4, 2, 3, and 0. The K-Medoids algorithm effectively identifies the most needy community groups, supporting targeted and efficient decisions in aid distribution.
Implementation of Transfer Function ARIMA Model for Stock Price Prediction Azizah, Alisa Jihan; Prasetya, Dwi Arman; Hindrayani, Kartika Maulida; Fahrudin, Tresna Maulana
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.v6i2.1396

Abstract

Dynamic economic growth requires stable financing sources, one of which is through the capital market. In stock investment activities, risk and return are two fundamental aspects that are interrelated and must be carefully considered. The volatility of ASII stock prices, influenced by various factors including exchange rates, can create uncertainty in investment decision-making. This study aims to predict the stock price of PT Astra International Tbk (ASII) using a transfer function model approach that integrates the influence of the Indonesian rupiah to US dollar exchange rate as an external variable. The transfer function model is an extension of the ARIMA model that can measure the dynamic relationship between input and output variables. Based on the estimation results, the best model obtained has a transfer function order of (b,s,r) = (1,0,0) with a noise series of (p_n,q_n) = (1,0). The prediction results show that ASII stock price movements tend to be stable with a gradual decline over the next 20 days. Model evaluation demonstrates low error rates, with MAE of 84.19, RMSE of 110.37, and MAPE of 1.65%. These results indicate that the transfer function model is effective in modeling and predicting short-term stock prices with reasonably good accuracy.
Deteksi Sentimen Komentar Aplikasi Gobis Suroboyo dengan Metode Naive Bayes dan Metode Regresi Logistik Elmaliyasari, Shifa; Alzam, Muhammad Arsyad; Pratiwi, Nanda Aulia; Wara, Shindi Shella May; Hindrayani, Kartika Maulida
JDMIS: Journal of Data Mining and Information Systems Vol. 3 No. 2 (2025): August 2025
Publisher : Yayasan Pendidikan Penelitian Pengabdian Algero

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54259/jdmis.v3i2.4691

Abstract

This research discusses sentiment analysis of user comments on the Gobis Suroboyo application using the Naive Bayes algorithm and Logistic Regression. Data was obtained through web scraping method from Google Play Store, with a total of 1,015 comments which then went through text pre-processing such as data cleaning, case folding, stemming, normalisation, filtering, tokenizing, and feature selection using TF-IDF. Sentiment labels were determined based on user ratings, with ratings above 3 as positive and 3 and below as negative. The results show that the Naive Bayes algorithm is better at classifying positive sentiment with a precision of 81% and f1-score of 77%, while Logistic Regression excels at negative sentiment with a precision of 82% and f1-score of 82%. The WordCloud visualisation shows dominant words such as “app”, “good”, and “bus stop” that reflect users attention to the app features and transportation services. The findings show that both algorithms have competitive and reliable performance for evaluating public opinion on comment-based digital services. This research is expected to be a reference for app developers and local governments in improving the quality of digital public services.
STOCK PRICE PREDICTION IN INDONESIA USING EXTREME GRADIENT BOOSTING OPTIMIZED BY ADAPTIVE PARTICLE SWARM OPTIMIZATION Safira, Alya Mirza; Trimono, Trimono; Hindrayani, Kartika Maulida
MEDIA STATISTIKA Vol 18, No 1 (2025): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/medstat.18.1.105-115

Abstract

High volatility is a major problem in generating accurate predictions of stock prices. It also causes unstable predictions and increases the loss risk. Therefore, an adaptive prediction model that is able to adjust to dynamic data pattern changes is needed. This study aims to address these issues by developing an Extreme Gradient Boosting (XGBoost) model optimized using Adaptive Particle Swarm Optimization (APSO). XGBoost was chosen for its ability to handle nonlinear relationships and minimize overfitting, while APSO serves to adaptively adjust parameters to obtain the optimal combination of hyperparameters. The novelty of this research lies in the application of XGBoost-APSO integration in the context of stock price prediction in the Indonesian capital market, which is characterized by high volatility. The study was conducted using daily closing price data of PT Aneka Tambang Tbk (ANTM) shares from November 2020 to May 2025 to predict prices seven days ahead. The results show that the XGBoost-APSO model provides the best performance with a MAPE value of 0.2%, superior to XGBoost-PSO (2.58%) and standard XGBoost (2.91%). This approach effectively improves prediction accuracy and supports quick and accurate investment decision making, while contributing to the development of intelligent prediction systems in the Indonesian capital market.
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.
Implementation of Web Scraping on Google Search Engine for Text Collection Into Structured 2D List Fahrudin, Tresna Maulana; Riyantoko, Prismahardi Aji; Hindrayani, Kartika Maulida
Telematika Vol 20 No 2 (2023): Edisi Juni 2023
Publisher : Jurusan Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/telematika.v20i2.9575

Abstract

Purpose: This research proposes the implementation of web scraping on Google Search Engine to collect text into a structured 2D list.Design/methodology/approach: Implementing two important stages in the process of collecting data through web scraping, namely the HTML parsing process to extract links (URL) on Google Search Engine pages, and HTML parsing process to extract the body text from website pages on each link that has been collected.Findings/result: The inputted query is adjusted to the latest issues and news in Indonesia, for example the President's important figures, the month of Ramadan and Idul Fitri, riots tragedy (stadium) and natural disasters, rising prices of basic commodities, oil and gold, as well as other news. The least number of links obtained was 56 links and the most was 151 links, while the processing time to obtain links for each of the fastest queries was 1 minute 6.3 seconds and the longest was 2 minutes 49.1 seconds. The results of scraping links from these queries were obtained from Wikipedia, Detik, Kompas, the Election Supervisory Body (Bawaslu), CNN Indonesia, the General Election Commission (KPU), Pikiran Rakyat, and others.Originality/value/state of the art: Based on previous research, this study provides an alternative to produce optimal collection of links and text from web scraping results in the form of a 2D list structure. Lists in the Python programming language can store character sequences in the form of strings and can be accessed using index keys, and manipulate text efficiently.
Penguatan Tata Kelola Pengadaan Barang dan Jasa di Perguruan Tinggi melalui Sistem Quotation dan Tender Digital Hindrayani, Kartika Maulida; Alfiansyah , Achmad Dzulfiqar; Putro, R. Kokoh H.
Joong-Ki : Jurnal Pengabdian Masyarakat Vol. 5 No. 1: November 2025
Publisher : CV. Ulil Albab Corp

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56799/joongki.v5i1.11401

Abstract

Kegiatan pengabdian kepada masyarakat ini bertujuan untuk memperkuat tata kelola pengadaan barang dan jasa di perguruan tinggi melalui penerapan sistem quotation dan tender digital. Program dilaksanakan di Unit Pengelolaan Pengadaan Barang dan Jasa (UPPBJ) UPN “Veteran” Jawa Timur dengan pendekatan partisipatif-kolaboratif, mencakup tahapan analisis kebutuhan, perancangan, pengembangan, pelatihan, uji coba, dan pendampingan implementasi. Sistem yang dikembangkan mengintegrasikan fitur e-quotation dan e-tendering dengan memperhatikan kemudahan penggunaan, keamanan data, dan kepatuhan terhadap regulasi nasional. Hasil kegiatan menunjukkan peningkatan pemahaman dan keterampilan pengguna dalam memanfaatkan teknologi untuk proses pengadaan yang lebih transparan, efisien, dan akuntabel. Dokumentasi kegiatan memperlihatkan keterlibatan aktif mitra dalam diskusi dan pelatihan, serta komitmen untuk mengadopsi sistem secara berkelanjutan. Kegiatan ini diharapkan menjadi model penerapan good governance dalam pengadaan barang dan jasa di lingkungan perguruan tinggi.
Categorical Boosting and Bayesian Optimization in Natural Disaster Tweet Classification Christina, Enzelica Vica; Saputra, Wahyu S. J.; Hindrayani, Kartika Maulida
Parameter: Jurnal Matematika, Statistika dan Terapannya Vol 4 No 2 (2025): Parameter: Jurnal Matematika, Statistika dan Terapannya
Publisher : Jurusan Matematika FMIPA Universitas Pattimura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/parameterv4i2pp339-352

Abstract

Multi-label classification is an important challenge in natural language processing, especially when a single text data point can have more than one label. This study applies a multi-label classification approach to group information in Twitter comments related to natural disasters in Indonesia. The data is categorized into six labels: disaster, location, damage, victims, aid, and others. To address the complexity of text data, the Categorical Boosting (CatBoost) algorithm is used, which is a decision tree-based boosting method that excels at handling categorical features and reducing overfitting. The model is built using the MultiOutputClassifier approach to handle multiple labels simultaneously. Additionally, Bayesian optimization is performed, which is a parameter search method that uses a probabilistic approach to select the best parameter combination based on previous evaluations. Optimization focused on four main parameters: number of iterations, learning rate, tree depth, and L2 regularization. The results showed that the model achieved an accuracy of 75.41% and a Hamming loss of 0.0520, demonstrating the effectiveness of this approach in handling multi-label classification on Twitter data.
Co-Authors Afidria, Zulfa Febi Ahmad, Davin Anezta Alfiansyah , Achmad Dzulfiqar Altetiko, Faizal Johan Alzam, Muhammad Arsyad Amelia, Meisya Vira Azizah, Alisa Jihan Bhalqis, Anissa Andiar Budi, Aditya Septa Christina, Enzelica Vica Diyasa, I Gede Susrama Mas Dwi Arman Prasetya Elmaliyasari, Shifa Eristya Maya Safitri Fahrudin, Tresna Maulana Fajar Ramadhani Fernando, Moch. Firman Fitriani, Aulia Nur idhom, Mohammad Isyanto, Aisyah Kirana Putri Made Hanindia Prami Swari Maulana F, Tresna Ningrum, Imelda Widya Nur Aini Rakhmawati Pakpahan, Vera Febrianti Pratiwi, Nanda Aulia Prismahardi Aji Riyantoko Prismahardi Aji Riyantoko Putro, R. Kokoh H. rachmanto, Nugroho Fajar Riskiyah, Ameliyah Riyantoko, Prismahardi Aji Safira, Alya Mirza Saputra, Wahyu S. J. Saputra, Wahyu Syaifullah Jauharis Tresna Maulana Fahruddin Tresna Maulana Fahrudin Trimono Trimono, Trimono Wara, Shindi Shella May Wardati, Nanda Kurnia Wibowo, Muhammad Bagas Satrio