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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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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.
Prediksi Volatilitas Saham KINO dan MRAT menggunakan Model BEKK-MGARCH Renaldy Al Ikhsan; Wahyu Syaifullah Jauharis Saputra; Kartika Maulida Hindrayani
JASIEK (Jurnal Aplikasi Sains, Informasi, Elektronika dan Komputer) Vol. 7 No. 1 (2025): Juni 2025
Publisher : Universitas Merdeka Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26905/jasiek.v7i1.15639

Abstract

This study analyzes the volatility prediction of KINO and MRAT stocks using the BEKK-MGARCH model during January 2019 to December 2024. Both stocks exhibit high price fluctuations, with volatility significantly influenced. Persistence effects are more dominant, with asymmetric spillover where KINO's influence on MRAT is stronger. Conditional correlation shows a shift from positive to negative in the 30-day forecast. Model evaluation demonstrates low RMSE values of 2.05×10⁻⁵ (KINO), 3.38×10⁻⁵ (MRAT), and 4.42×10⁻⁵ (covariance), indicating excellent predictive performance and confirming the reliability of the BEKK-MGARCH model with exponential smoothing in forecasting the volatility dynamics and relationship between these two stocks with high precision. However, the Jarque-Bera test rejects residual normality (p < 2.2×10⁻¹⁶), and the Ljung-Box test detects autocorrelation, suggesting the need for more complex models such as Student-t distribution or asymmetric models. These findings provide important insights for investors in managing risk and portfolio diversification strategies in the cosmetics sector.
Komparasi Hasil Segmentasi Metode K-Means dan Agglomerative Hierarchical Terhadap Provinsi di indonesia Berdasarkan Profil Perjalanan Wisata Tahun 2024 Ni Luh Ayu Nariswari Dewi; Azizah Zalfa Assyadida; Steffany Marcellia Witanto; Muhammad Nasrudin; Kartika Maulida Hindrayani
STATMAT : JURNAL STATISTIKA DAN MATEMATIKA Vol 7 No 3 (2025)
Publisher : Department of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Pamulang, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32493/sm.v7i3.49999

Abstract

Indonesia merupakan negara dengan kekayaan alam dan budaya yang beragam sehingga memiliki potensi pariwisata yang sangat besar. Salah satu faktor penting dalam pertumbuhan sektor pariwisata adalah pergerakan wisatawan nusantara. Kegiatan wisata yang dilakukan oleh wisatawan nusantara memiliki berbagai tujuan, seperti liburan, kunjungan keluarga, keagamaan, maupun urusan pekerjaan. Keanekaragaman tersebut mencerminkan adanya perbedaan karakteristik lokasi wisata di setiap provinsi sehingga diperlukan analisis lebih lanjut untuk mengelompokkan provinsi berdasarkan profil perjalanan wisata. Penelitian ini bertujuan untuk membandingkan hasil segmentasi menggunakan metode K-Means dan Agglomerative Hierarchical Clustering (AHC) terhadap provinsi di Indonesia berdasarkan data perjalanan wisata tahun 2024 yang bersumber dari Badan Pusat Statistik (BPS). Evaluasi hasil cluster dengan metode K-Means menunjukkan terbentuknya 3 cluster dengan Silhouette Score sebesar 0,662. Sedangkan, dengan metode Agglomerative Hierarchical Clustering (AHC) terbentuk 3 cluster yang memiliki nilai Silhouette Score sebesar 0,9535 menggunakan pemilihan jarak average linkage. Hal tersebut menunjukkan bahwa objek atau data sudah berada pada cluster yang sesuai. Indonesia is a country with diverse natural and cultural resources, giving it enormous tourism potential. One important factor in the growth of the tourism sector is the movement of domestic tourists. Domestic tourists engage in various types of tourism activities, such as vacations, family visits, religious pilgrimages, and business trips. This diversity reflects the differing characteristics of tourist destinations across provinces, necessitating further analysis to group provinces based on travel profiles. This study aims to compare the results of segmentation using the K-Means method and Agglomerative Hierarchical Clustering (AHC) for provinces in Indonesia based on 2024 travel data sourced from the Central Statistics Agency (BPS). The evaluation of the cluster results using the K-Means method shows the formation of 3 clusters with a Silhouette Score of 0.662. Meanwhile, using the Agglomerative Hierarchical Clustering (AHC) method, 3 clusters were formed with a Silhouette Score of 0.9535 using the average linkage distance selection. This indicates that the objects or data are already in the appropriate clusters.
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.
ENHANCED CLUSTERING USING PSO-KMEDOIDS FOR GOVERNMENT AID DISTRIBUTION Aulia Nur Fitriani; Kartika Maulida Hindrayani; 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/simika.v8i2.3930

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.
PREDIKSI HARGA SAHAM DI INDONESIA DENGAN EXTREME GRADIENT BOOSTING YANG DIOPTIMALKAN OLEH ADAPTIVE PARTICLE SWARM OPTIMIZATION Alya Mirza Safira; Trimono Trimono; Kartika Maulida Hindrayani
Jurnal Sistem Informasi dan Informatika (Simika) Vol. 9 No. 1 (2026): 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: 10.47080/5r67ag12

Abstract

Volatile stock price movements are a big problem in making investment decisions, especially in stocks with high volatility. This research aims to build a stock price prediction model by utilising the Extreme Gradient Boosting (XGBoost) algorithm optimised using the Adaptive Particle Swarm Optimization (APSO) method. The research focuses on two stocks with high volatility levels, namely PT Jaya Agra Wattie Tbk (JAWA) and PT Charnic Capital Tbk (NICK) with historical closing price data from August 2019 to July 2024. The research process includes data collection, preprocessing, modelling, optimazing and model performance evaluation using the Mean Absolute Percentage Error (MAPE) metric. The results showed that the XGBoost-APSO combination proved superior to the standard XGBoost-PSO and XGBoost methods in predicting stock prices without overfitting. The MAPE value on JAWA stock is 5.20 (training) and 5.95 (testing) with a difference of 0.75. As for NICK stock, the MAPE on training data is 4.50 and testing is 5.40, with a difference of 0.90. The model also successfully predicts the closing price movement in the next five days realistically according to its historical volatility characteristics. This research proves that the combination of XGBoost with APSO optimisation is effective in handling stock data with high volatility and can be used as a predictive tool in investment decision making. Keyword: prediction, stock price, volatile, XGBoost, APSO Data, Source Code, dan Plagiarisme: https://drive.google.com/file/d/1GRhEguXHYj-mzbE2fW7MsnpjUuaJnyxl/view?usp=sharing
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