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All Journal Media Statistika JURNAL SISTEM INFORMASI BISNIS Telematika : Jurnal Informatika dan Teknologi Informasi Seminar Nasional Informatika (SEMNASIF) G-Tech : Jurnal Teknologi Terapan 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 Data Science, Engineering, and Analytics (IJDASEA) 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 Jurnal PETISI (Pendidikan Teknologi Informasi) Joong-Ki
Kartika Maulida Hindrayani
Faculty Of Computer Science, Universitas Pembangunan Nasional “Veteran” Jawa Timur, Surabaya 60294, Indonesia
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 Energy 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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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.
Sentiment Analysis on Generation Z News Article using Support Vectore Machine (SVM) with Synthetic Minority Over-sampling Technique (SMOTE) Kartini, Kartini; Hindrayani, Kartika Maulida; Puspasari, Betty Dewi
IJCONSIST JOURNALS Vol 5 No 2 (2024): March
Publisher : International Journal of Computer, Network Security and Information System

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33005/ijconsist.v5i2.141

Abstract

The development of digital media has increased the volume of news articles discussing various issues, including those involving Generation Z. Understanding public perception of these news items can be achieved by applying a crucial approach, namely sentiment analysis. This study aims to classify sentiment in news articles about Generation Z using the Support Vector Machine (SVM) algorithm. The main challenge in sentiment analysis is data class imbalance, where the amount of positive and negative sentiment data is often unbalanced. Therefore, the Synthetic Minority Over-sampling Technique (SMOTE) is used to address this problem by balancing the class distribution before model training. The datasets used were collected from various online news portals and analyzed through text preprocessing, feature extraction using Bag of Word, and SVM model training. The evaluation results show that the application of SMOTE significantly improves the model's performance in classifying sentiment, with improvements in accuracy, precision, recall, and F1-score compared to the model without data imbalance handling. This study demonstrates that the combination of SVM and SMOTE is effective in conducting sentiment analysis on Generation Z news articles. The accuracy shows 84% with 83% precision and 76% recall.
Pengujian Fungsional Website Crusher Report Berbasis Machine Learning Menggunakan Metode Robustness Testing Adhigiadany, Chelsea Ayu; Hindrayani, Kartika Maulida; Prasetya, Dwi Arman
JURNAL PETISI (Pendidikan Teknologi Informasi) Vol. 7 No. 1 (2026): JURNAL PETISI (Pendidikan Teknologi Informasi)
Publisher : Universitas Pendidikan Muhammadiyah Sorong

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36232/jurnalpetisi.v7i1.2014

Abstract

Website dan Machine Learning menjadi kebutuhan penting perusahaan dalam rangka meningkatkan efektivitas kinerja. Salah satu implementasi integrasi website dengan Machine Learning adalah website Crusher Report milik PT XYZ. Website yang dirancang dengan memanfaatkan LARS, PostgreSQL, dan Flask ini sudah diuji secara ketangkasan model dalam memprediksi. Penelitian ini bertujuan untuk menguji keandalan website Crusher Report sebagai user interface milik PT XYZ menggunakan pendekatan Black Box Testing dengan metode Robustness Testing. Skenario pengujian yang digunakan yaitu dengan memberikan input diluar ketentuan website. Hasil pengujian menunjukkan bahwa website mampu menangani seluruh input tidak valid dengan baik melalui notifikasi kesalahan dan pengaturan nilai input otomatis, menghasilkan tingkat keberhasilan pengujian sebesar 100%. Temuan ini menunjukkan bahwa website Crusher Report efektif dalam mendeteksi dan mengelola kesalahan input, serta layak digunakan sebagai platform pendukung operasional crusher PT XYZ.
Implementasi Metode Ensemble ROCK dalam Pengelompokan UMKM di Kabupaten Malang Purwadwika, Reza Sadiya; Hindrayani, Kartika Maulida; Damaliana, Aviolla Terza
JURNAL PETISI (Pendidikan Teknologi Informasi) Vol. 7 No. 1 (2026): JURNAL PETISI (Pendidikan Teknologi Informasi)
Publisher : Universitas Pendidikan Muhammadiyah Sorong

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36232/jurnalpetisi.v7i1.3396

Abstract

UMKM memiliki peran penting dalam perekonomian nasional, namun masih menghadapi berbagai permasalahan seperti rendahnya pemanfaatan teknologi, keterbatasan akses permodalan, dan lemahnya daya saing. Kompleksitas karakteristik data UMKM yang mencakup variabel numerik dan kategorikal menjadi tantangan dalam analisis dan pemetaan yang akurat. Penelitian ini bertujuan untuk mengelompokkan UMKM di Kabupaten Malang berdasarkan karakteristik usaha dan pelaku usahanya dengan pendekatan ensemble clustering menggunakan algoritma ROCK. Data terdiri dari 75 entri UMKM yang mencakup variabel numerik (omset, modal, tenaga kerja) dan kategorikal (jenis usaha, penggunaan aplikasi transportasi daring). Clustering dilakukan secara terpisah dengan Agglomerative Hierarchical Clustering untuk data numerik dan ROCK untuk data kategorikal. Hasil kedua metode digabungkan menggunakan pendekatan ensemble untuk memperoleh klaster yang lebih stabil dan representatif. Parameter optimal diperoleh pada theta = 0,05 dan k = 4 dengan nilai Clustering Purity (CP*) sebesar 0,8148 dan Davies-Bouldin Index sebesar 0,3817, menunjukkan pemisahan cluster yang baik. Cluster akhir menunjukkan perbedaan signifikan dalam skala usaha, pemanfaatan teknologi digital, dan performa ekonomi. Temuan ini diharapkan menjadi dasar dalam merancang kebijakan pengembangan UMKM yang lebih tepat sasaran dan berbasis data.
Analisis Sentimen Ulasan Aplikasi Maxim Merchant dengan Support Vector Machine (SVM) dan Random Forest Rizkiyah, Selly; Rizqin, Indira Zein; Putri, Milla Akbarany Baktiar; Wara, Shindi Shella May; Hindrayani, Kartika Maulida
JDMIS: Journal of Data Mining and Information Systems Vol. 4 No. 1 (2026): February 2026
Publisher : Yayasan Pendidikan Penelitian Pengabdian Algero

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54259/jdmis.v4i1.4765

Abstract

The development of digital technology, especially mobile devices, has led to an increase in application-based services. One important aspect in app development is to deeply understand user perception and satisfaction. This study aims to analyze user sentiment towards the Maxim Merchant application based on reviews obtained from the Google Play Store platform. A total of more than 2800 Indonesian-language reviews were collected using web scraping techniques. The review data was processed through pre-processing stages such as text cleaning, normalization, tokenization, removal of unimportant words, and stemming. Sentiments are categorized into positive and negative based on the review score, where scores of 1 to 3 are considered negative, and scores of 4 and 5 are considered positive. Word cloud visualization is used to show the dominant words of each sentiment category. The data is then converted into numerical form using TF-IDF and selected using the Chi-Square method. Classification was performed using Support Vector Machine and Random Forest algorithms. The evaluation results show that the Support Vector Machine algorithm performs better in classifying sentiment, especially in handling high-dimensional text data.
Co-Authors Adhigiadany, Chelsea Ayu Afidria, Zulfa Febi Ahmad, Davin Anezta Aisyah Kirana Putri Isyanto Alfiansyah , Achmad Dzulfiqar Altetiko, Faizal Johan Alzam, Muhammad Arsyad Amelia, Meisya Vira Amri Muhaimin Aviolla Terza Damaliana Azizah, Alisa Jihan Betty Dewi Puspasari Bhalqis, Anissa Andiar Brescia Ayundina Yuniarossy Budi, Aditya Septa Burhan Syarif Acarya Christina, Enzelica Vica Damaliana, Aviolla Terza Diyasa, I Gede Susrama Mas Dwi Arman Prasetya Elmaliyasari, Shifa Eristya Maya Safitri Fahrudin, Tresna Maulana Fajar Ramadhani Fernando, Moch. Firman idhom, Mohammad Imelda Widya Ningrum Isyanto, Aisyah Kirana Putri Kartini Kartini Made Hanindia Prami Swari Maulana F, Tresna Mohammad Idhom Ningrum, Imelda Widya Nur Aini Rakhmawati Pakpahan, Vera Febrianti Pratiwi, Nanda Aulia Prismahardi Aji Riyantoko Purwadwika, Reza Sadiya Putri, Milla Akbarany Baktiar Putro, R. Kokoh H. rachmanto, Nugroho Fajar Riskiyah, Ameliyah Risnaldy Novendra Irawan Riyantoko, Prismahardi Aji Rizkiyah, Selly Rizqin, Indira Zein Safira, Alya Mirza Saputra, Wahyu S. J. Saputra, Wahyu Syaifullah Jauharis Shindi Shella May Wara Tresna Maulana F Tresna Maulana Fahruddin Tresna Maulana Fahrudin Tresna Maulana Fahrudin Trimono Trimono, Trimono Wardati, Nanda Kurnia Wibowo, Muhammad Bagas Satrio