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All Journal Media Statistika JURNAL SISTEM INFORMASI BISNIS Telematika : Jurnal Informatika dan Teknologi Informasi Jurnal Teknologi Informasi dan Ilmu Komputer Seminar Nasional Informatika (SEMNASIF) JOURNAL OF APPLIED INFORMATICS AND COMPUTING PINTER : Jurnal Pendidikan Teknik Informatika dan Komputer JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) JASIEK (Jurnal Aplikasi Sains, Informasi, Elektronika dan Komputer) bit-Tech Jurnal Sistem Informasi dan Informatika (SIMIKA) Jurnal Informasi dan Teknologi JATI (Jurnal Mahasiswa Teknik Informatika) G-Tech : Jurnal Teknologi Terapan International Journal of Advances in Data and Information Systems ESTIMASI: Journal of Statistics and Its Application Jurnal Statistika dan Matematika (Statmat) Journal of Advanced in Information and Industrial Technology (JAIIT) Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika Nusantara Science and Technology Proceedings Jurnal Teknik Informatika (JUTIF) HOAQ (High Education of Organization Archive Quality) : Jurnal Teknologi Informasi Journal of Technology and Informatics (JoTI) International Journal of Data Science, Engineering, and Analytics (IJDASEA) International Journal Of Computer, Network Security and Information System (IJCONSIST) Journal of Information Systems and Technology Research Journal of International Conference Proceedings Jurnal Teknik Terapan (J-TETA) Journal of Data Mining and Information Systems Parameter: Jurnal Matematika, Statistika dan Terapannya Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Jurnal PETISI (Pendidikan Teknologi Informasi) Joong-Ki Jurnal Pengabdian Masyarakat SENSASI
Kartika Maulida Hindrayani
Universitas Pembangunan Nasional Veteran Jawa Timur
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PERBANDINGAN ARSITEKTUR VANILLA, STACKED, DAN BIDIRECTIONAL LONG SHORT-TERM MEMORY UNTUK PREDIKSI PERIODE MUSIM DI SURABAYA Sinthya Putri, Diana; Syaifullah Jauharis Saputra, Wahyu; Maulida Hindrayani, Kartika
JATI (Jurnal Mahasiswa Teknik Informatika) Vol. 9 No. 3 (2025): JATI Vol. 9 No. 3
Publisher : Institut Teknologi Nasional Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36040/jati.v9i3.13769

Abstract

Indonesia memiliki dua musim utama, namun perubahan iklim global menyebabkan pergeseran pola musim yang tidak menentu. Hal ini berdampak pada berbagai sektor, termasuk agribisnis, transportasi, dan konstruksi. Surabaya, sebagai pusat ekonomi di Jawa Timur, memerlukan prediksi musiman yang akurat untuk mitigasi risiko dan perencanaan strategis. Penelitian ini mengevaluasi kinerja tiga arsitektur Long Short-Term Memory (LSTM), yaitu Vanilla LSTM, Stacked LSTM, dan Bidirectional LSTM, dalam memprediksi pola musiman curah hujan di Surabaya. Data yang digunakan berasal dari BMKG Stasiun Meteorologi Maritim Tanjung Perak, mencakup periode 2001-2024. Hasil eksperimen menunjukkan bahwa Bidirectional LSTM mencapai nilai MAE terendah sebesar 25,7883, diikuti oleh Stacked LSTM dengan MAE 26,5515, dan Vanilla LSTM dengan MAE 27,7023. Temuan ini mengkonfirmasi bahwa arsitektur yang lebih dalam dan kompleks, seperti Stacked LSTM dan Bidirectional LSTM, mampu meningkatkan akurasi prediksi secara signifikan dibandingkan Vanilla LSTM.
PENERAPAN METODE MEAN SHIFT CLUSTERING UNTUK MENGELOMPOKKAN WILAYAH BERDASARKAN PENGELOLAAN SAMPAH Lidya Musaffak, Awal; Maulida Hindrayani, Kartika; Idhom, Mohammad
JATI (Jurnal Mahasiswa Teknik Informatika) Vol. 9 No. 3 (2025): JATI Vol. 9 No. 3
Publisher : Institut Teknologi Nasional Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36040/jati.v9i3.13777

Abstract

Pengelolaan sampah di Indonesia menjadi tantangan besar dengan meningkatnya timbulan sampah setiap tahun. Data SIPSN 2023 mencatat timbulan sampah harian sebesar 106.145,71 ton dan tahunan mencapai 38.743.185,18 ton. Setiap wilayah memiliki pola pengelolaan sampah yang berbeda, sehingga diperlukan segmentasi untuk memahami variasinya. Penelitian ini menerapkan algoritma Mean Shift Clustering untuk mengelompokkan wilayah berdasarkan data pengurangan dan penanganan sampah di setiap kabupaten dan kota. Dengan bandwidth 1.5, hasil analisis menunjukkan terbentuknya dua klaster dengan nilai Silhouette Score sebesar 0.649. Terdapat dua klaster yang dihasilkan dengan klaster 1 merupakan klaster dengan sampah yang terkelola rendah sedangkan klaster 2 adalah klaster dengan sampah terkelola tinggi. Hasil penelitian ini diharapkan dapat membantu dalam perumusan kebijakan yang lebih tepat sasaran untuk meningkatkan pengelolaan sampah secara efisien dan berkelanjutan di berbagai daerah.
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 ULASAN APLIKASI SMILE INDONESIA MENGGUNAKAN METODE NAIVE BAYES DAN SUPPORT VECTOR MACHINE (SVM): SENTIMENT ANALYSIS OF SMILE INDONESIA APPLICATION REVIEWS USING NAIVE BAYES AND SUPPORT VECTOR MACHINE (SVM) METHODS Rhomaningtias, Lina; Khairunisa, Adenda; Shella May Wara, Shindi; Maulida Hindrayani, Kartika
HOAQ (High Education of Organization Archive Quality) : Jurnal Teknologi Informasi Vol. 16 No. 1 (2025): Jurnal HOAQ - Teknologi Informasi
Publisher : STIKOM Uyelindo Kupang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52972/hoaq.vol16no1.p79-91

Abstract

Tujuan studi ini adalah untuk mengevaluasi bagaimana pengguna memandang aplikasi digital SMILE Indonesia, sebuah platform layanan publik yang memantau penyampaian layanan kesehatan secara real-time. Menggunakan teknik web scraping, 383 ulasan pengguna dikumpulkan dari Google Play Store dan secara otomatis diklasifikasikan berdasarkan skor penilaian: ulasan dengan skor 1-2 dikategorikan sebagai negatif, ulasan dengan skor 4-5 sebagai positif, dan ulasan dengan skor 3 atau lebih rendah dikecualikan karena kemungkinan ambiguitas. Langkah-langkah pre-processing seperti case folding, pembersihan teks, tokenisasi, penghapusan kata, stemming, dan normalisasi diterapkan pada data yang telah dilabeli. Metode TF-IDF (Term Frequency–Inverse Document Frequency) kemudian digunakan untuk mewakili data secara numerik. Dua algoritma digunakan untuk klasifikasi: Naïve Bayes dan Support Vector Machine (SVM). Hasil evaluasi menunjukkan bahwa SVM mencapai 75% pada keempat metrik, sementara Naïve Bayes mencapai akurasi 79%, presisi 81%, recall 79%, dan F1-score 79%. Uji McNemar menunjukkan bahwa perbedaan kinerja antara kedua model tidak signifikan secara statistik (p > 0.05), meskipun Naïve Bayes memperoleh skor yang lebih tinggi. Penelitian sentimen ini memberikan wawasan tentang bagaimana masyarakat umum memandang layanan publik digital; sementara sikap negatif menekankan kesulitan teknis, sikap positif menyoroti aksesibilitas dan keuntungan praktis. Hasil ini dapat digunakan secara strategis oleh pengembang dan pembuat kebijakan untuk meningkatkan kualitas layanan digital berbasis e-government, terutama di bidang logistik kesehatan. The purpose of this study is to evaluate how users perceive the SMILE Indonesia digital application, a public service platform that monitors the delivery of health services in real time. Using web scraping techniques, 383 user reviews were collected from the Google Play Store and automatically classified based on rating scores: reviews with scores of 1-2 were categorized as negative, reviews with scores of 4-5 as positive, and reviews with scores of 3 or lower were excluded due to potential ambiguity. Pre-processing steps such as case folding, text cleaning, tokenization, word removal, stemming, and normalization were applied to the labeled data. The TF-IDF (Term Frequency–Inverse Document Frequency) method was then used to represent the data numerically. Two algorithms were used for classification: Naïve Bayes and Support Vector Machine (SVM). Evaluation results show that SVM achieved 75% on all four metrics, while Naïve Bayes achieved 79% accuracy, 81% precision, 79% recall, and 79% F1-score. The McNemar test indicates that the performance difference between the two models is not statistically significant (p > 0.05), although Naïve Bayes achieved higher scores. This sentiment analysis provides insights into how the general public perceives digital public services; while negative attitudes emphasize technical difficulties, positive attitudes highlight accessibility and practical benefits. These results can be strategically utilized by developers and policymakers to improve the quality of e-government-based digital services, particularly in the field of health logistics.
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.
Optimizing Categorical Boosting Model with Optuna for Anti-Tuberculosis Drugs Classification Yosua Satria Bara Harmoni; Kartika Maulida Hindrayani; Dwi Arman Prasetya
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 7 No. 2 (2025): May
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/ijeeemi.ijeeemi.v7i2.92

Abstract

Tuberculosis is one of the leading causes of death globally, with death rate reaching 1.30 million by 2022, an increase of 3.2% compared to the previous year. Indonesia is one of the countries with the highest number of tuberculosis cases in the world. The Directly Observed Treatment Short-course (DOTS) plays a role in improving the effectiveness of tuberculosis therapy by ensuring the availability of appropriate anti-tuberculosis drugs. However, errors in drug selection can lead to therapy failure, relapse, and Multi-Drug Resistant (MDR) cases. To overcome this, classification models based on patient medical record data can be used to improve the accuracy of drug selection. This research focuses on developing classification model to determine the type of drug using Categorical Boosting algorithm optimized with Optuna using Tree-structured Parzen Estimator. The data consisted of numerical variables, such as age, treatment duration, and categorical variables, such as history of diabetes mellitus, HIV status, drug combination. The CatBoost algorithm was chosen due to its ability to handle categorical data. Hyperparameter optimization was performed to obtain the best parameters. The preprocessing stage involved memory reduction, feature normalization, and encoding on 620 data samples, which were then divided into 90% training and 10% test data. Experimental results show CatBoost model produces an initial accuracy of 90%. After applying parameter optimization techniques using Optuna, the accuracy increased to 96%, showing 6% improvement. The model is able to accurately classify drugs combination, which can support the selection of more effective therapies for tuberculosis patients. Thus, the use of SMOTE to address class imbalance combined with Optuna for hyperparameter optimization was shown to improve the accuracy of CatBoost-based classification models. This finding confirms the effectiveness of SMOTE and Optuna methods in improving the accuracy of prediction models for drug type classification, contributing the improvement of tuberculosis treatment strategies.
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.
Implementasi Algoritma LightGBM untuk Prediksi Status Gizi Bayi dan Balita di Desa Doko Kabupaten Kediri Thoriqulhaq, Muhammad; Idhom, Mohammad; Maulida Hindrayani, Kartika
Jurnal Teknik Terapan Vol. 4 No. 2 (2025): Oktober
Publisher : P3M Politeknik Negeri Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

MThe issue of nutritional status among infants and toddlers remains a serious concern in Indonesia, particularly in rural areas. Doko Village was chosen as the research location due to its significant challenges in child health. This study aims to develop a nutritional status prediction model based on the LightGBM algorithm, capable of processing anthropometric data to classify nutritional categories such as "Underweight", "Normal", and "Overweight". Using an 80:20 training-to-testing data ratio, the model achieved 97% accuracy and a 94% F1-score. In addition to building the prediction model, this study also developed an interactive web application using Streamlit, and compared its results with the conventional WHO AnthroPlus method. The results indicate that LightGBM offers advantages in terms of speed, flexibility, and predictive accuracy based on local data.
Co-Authors Aang Kisnu Darmawan Abdul Mukti Achmad Dzulfiqar Alfiansyah Adhigiadany, Chelsea Ayu Afidria, Zulfa Febi Ahmad, Davin Anezta Aisyah Kirana Putri Isyanto Aji R, Prismahardi Altetiko, Faizal Johan Alya Mirza Safira Alzam, Muhammad Arsyad Amanda Aulia Amelia, Meisya Vira Amri Muhaimin Ardia Eva Ardiani Arkananta Handoyo Aulia Nur Fitriani Aviolla Terza Damaliana Azizah Zalfa Assyadida Azizah, Alisa Jihan Betty Dewi Puspasari Bhalqis, Anissa Andiar Brescia Ayundina Yuniarossy Budi, Aditya Septa Burhan Syarif Acarya Chelsea Ayu Adhigiadany Christina Halim Christina, Enzelica Vica Damaliana, Aviolla Terza Diyasa, I Gede Susrama Mas Dwi Arman Prasetya Dwi Arman Prasetya Dwi Arman Prasetya Dwi Arman Prasetya Edelin Fortuna Elmaliyasari, Shifa Endang Tri Wahyurini Fahrudin, Tresna Maulana Fajar Ramadhani Fajria Ulumin Nafiah Fernando, Moch. Firman Hilya Zada Mardhatilla Al Haadiy Holly Patrycia I Gede Susrama Mas Diayasa idhom, Mohammad Imam Imanta Ginting Imelda Widya Ningrum Indira Zein Rizqin Isyanto, Aisyah Kirana Putri Kartini Kartini Kartini Kartini Khairunisa, Adenda Kristananda, Raja Valentino Lidya Musaffak, Awal Made Hanindia Prami Swari Maudi Adella Maulana F, Tresna Meisya Vira Amelia Meisya Vira Amelia Milla Akbarany Baktiar Putri Mohammad Idhom Mohammad Idhom Mohammad Idhom Muhammad Rafli Muhimmatul Arofah Nanda Kurnia Wardati Ni Luh Ayu Nariswari Dewi Ningrum, Imelda Widya Ningrum, Lisya Septyo Nur Aini Rakhmawati Pakpahan, Vera Febrianti Pratiwi, Nanda Aulia Prismahardi Aji Riyantoko Purwadwika, Reza Sadiya Putro, R. Kokoh H. rachmanto, Nugroho Fajar Radya Ardi Renaldy Al Ikhsan Reza Sadiya Purwadwika Rhomaningtias, Lina Riskiyah, Ameliyah Risnaldy Novendra Irawan Rizky Fatkhur Rohman Safira, Alya Mirza Safitri, Eristya Maya Saputra, Wahyu S. J. Selena Nurmanina Afandy Selly Rizkiyah Shindi Shella May Wara Shindi Shella May Wara Shindi Shella May Wara Sinthya Putri, Diana Steffany Marcellia Witanto Thoriqulhaq, Muhammad Tresna Maulana F Tresna Maulana Fahruddin Tresna Maulana Fahrudin Tresna Maulana Fahrudin Trimono Trimono Trimono Trimono Trimono Trimono Trimono Trimono Trimono, Trimono Wahyu Syaifullah Jauharis Saputra Wahyu Syaifullah JS Wibowo, Muhammad Bagas Satrio Yosua Satria Bara Harmoni