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All Journal Media Statistika JURNAL SISTEM INFORMASI BISNIS Telematika : Jurnal Informatika dan Teknologi Informasi Seminar Nasional Informatika (SEMNASIF) JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) bit-Tech 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.
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.
Intelligent Detection of Spermatozoa Motility Using YOLOv5: Toward Efficient and Accurate Male Fertility Analysis Christina Halim; Wahyu Syaifullah JS; Kartika Maulida Hindrayani; I Gede Susrama Mas Diayasa
bit-Tech Vol. 8 No. 2 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i2.3231

Abstract

Detecting multiple spermatozoa in microscopic videos remains a complex challenge due to their small size, high velocity, frequent overlap, and inconsistent illumination. This study introduces an enhanced real-time detection framework using the YOLOv5 deep learning algorithm, representing a significant advancement over previous Computer-Assisted Sperm Analysis (CASA) systems that primarily relied on classical image processing or earlier YOLO versions (e.g., YOLOv3, YOLOv4). Unlike these predecessors, the proposed YOLOv5-based model integrates Cross Stage Partial (CSP) architecture and optimized feature pyramid networks, allowing for superior detection of small, fast-moving spermatozoa with reduced computational complexity and model size. A curated dataset of sperm motility videos was processed through standardized steps—frame extraction, contrast enhancement, and manual annotation—to ensure uniformity and data quality. The model, trained via transfer learning on images of 640×640 pixels over 50 epochs, achieved a precision of 0.6333, recall of 0.627, and mAP@0.5 of 0.618, while maintaining real-time performance at 93 frames per second (FPS). Compared to YOLOv4, the proposed framework reduced training time by two-thirds (from 3 hours to 1 hour) and decreased model size from 244 MB to 13.8 MB, without compromising accuracy. These improvements establish YOLOv5 as a lightweight and scalable AI model for sperm detection, enabling automated, objective, and reproducible motility assessment. Clinically, this approach enhances the precision and consistency of male fertility diagnostics, paving the way toward AI-driven reproductive health evaluation and more accessible fertility screening solutions in both advanced and resource-limited laboratory settings.
OPTIMASI PUSAT CLUSTER K-PROTOTYPES PADA PENGELOMPOKAN PENERIMAAN BANTUAN REHABILITASI RUTILAHU DI KOTA SURABAYA Ningrum, Lisya Septyo; Hindrayani, Kartika Maulida; Jauharis Saputra, Wahyu Syaifullah
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 11, No 1 (2026)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v11i1.7656

Abstract

Clustering merupakan teknik penting dalam data mining yang digunakan untuk mengelompokkan data berdasarkan kesamaan karak-teristik. Algoritma K-Prototypes sering digunakan pada data bertipe campuran karena mengombinasikan K-Means untuk atribut numerik dan K-Modes untuk atribut kategorikal. Namun, kinerjanya sangat ber-gantung pada inisialisasi pusat klaster awal. Penelitian ini men-gusulkan penerapan tiga algoritma optimasi yaitu Particle Swarm Op-timization (PSO), Genetic Algorithm (GA), dan Flower Pollination Algorithm (FPA) untuk meningkatkan performa K-Prototypes dalam pengelompokan calon penerima program rehabilitasi Rumah Tidak Layak Huni (Rutilahu) di Kota Surabaya. Evaluasi dilakukan menggunakan Davies-Bouldin Index (DBI), Silhouette Score, dan wak-tu komputasi. Berdasarkan hasil penelitian menunjukkan bahwa PSO memberikan hasil terbaik dengan DBI terendah sebesar 0,6467, Silhou-ette Score tertinggi sebesar 0,5498, dan waktu komputasi tercepat yaitu 23,5168 detik. GA menghasilkan DBI tertinggi sebesar 0,7134, Silhou-ette Score sebesar 0,5143, serta waktu komputasi terlama yaitu 7220,6384 detik. FPA memiliki DBI 0,6467 dan Silhouette Score yang sama dengan PSO, tetapi dengan waktu komputasi sebesar 3415,9175 detik. Dengan demikian, PSO terbukti paling efektif dalam meningkat-kan akurasi dan efisiensi clustering K-Prototypes, serta mendukung distribusi bantuan yang lebih adil dan tepat sasaran.
Comparison of the Effectiveness IndoBERT and mBERT for Sentiment Analysis of SME Customer Reviews Afandy, Selena Nurmanina; Hindrayani, Kartika Maulida; Damaliana, Aviolla Terza
bit-Tech Vol. 8 No. 3 (2026): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i3.3501

Abstract

This study presents a structured comparative evaluation of IndoBERT and Multilingual BERT (mBERT) for three-class sentiment classification of customer reviews from Pawonkoe Banyuwangi, an Indonesian small and medium-sized enterprise (SME). Motivated by the limited transferability of IndoNLU-style benchmarks to real SME feedback, the central question is whether monolingual versus multilingual transformers remain reliable when fine-tuned on small, domain-specific, and operationally noisy datasets. A total of 365 survey-based reviews (January–December 2024), which is substantially smaller than typical transformer fine-tuning corpora, served as the empirical basis. Models were fine-tuned under matched hyperparameters and evaluated using a single stratified hold-out train–test split (not cross-validation), reporting accuracy, precision, recall, and F1-score. To reflect the deployed pipeline, mBERT additionally incorporates the original 1–5 rating as an auxiliary numeric signal alongside the review text, whereas IndoBERT is trained on text only. The results reveal a substantial performance gap: mBERT achieved 81% test accuracy, whereas IndoBERT reached 48% under the same evaluation setting. Because the label distribution is strongly imbalanced (with very few negative instances), these aggregate scores should be interpreted as overall effectiveness rather than minority-class robustness. Overall, the findings indicate that multilingual representations combined with auxiliary rating information can generalize more effectively in low-resource SME scenarios, while IndoBERT appears more sensitive to data scarcity in this context. The study offers practical guidance for model selection in resource-constrained Indonesian sentiment analytics and contributes evidence on transformer behavior beyond curated benchmarks.
Co-Authors Achmad Dzulfiqar Alfiansyah Adhigiadany, Chelsea Ayu Afandy, Selena Nurmanina Afidria, Zulfa Febi Ahmad, Davin Anezta Aisyah Kirana Putri Isyanto Altetiko, Faizal Johan Alzam, Muhammad Arsyad Amelia, Meisya Vira Amri Muhaimin Aviolla Terza Damaliana Azizah, Alisa Jihan Bhalqis, Anissa Andiar Brescia Ayundina Yuniarossy Budi, Aditya Septa Burhan Syarif Acarya Christina Halim 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 I Gede Susrama Mas Diayasa idhom, Mohammad Imelda Widya Ningrum Indira Zein Rizqin Isyanto, Aisyah Kirana Putri Jauharis Saputra, Wahyu Syaifullah Made Hanindia Prami Swari Maulana F, Tresna Milla Akbarany Baktiar Putri Mohammad Idhom 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 Rafli, Muhammad Riskiyah, Ameliyah Risnaldy Novendra Irawan Riyantoko, Prismahardi Aji Safira, Alya Mirza Saputra, Wahyu S. J. Saputra, Wahyu Syaifullah Jauharis Selly Rizkiyah Shindi Shella May Wara Shindi Shella May Wara Tresna Maulana F Tresna Maulana Fahruddin Tresna Maulana Fahrudin Tresna Maulana Fahrudin Trimono Trimono, Trimono Wahyu Syaifullah JS Wardati, Nanda Kurnia Wibowo, Muhammad Bagas Satrio