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All Journal Tech-E Jurnal Teknik Informatika UNIKA Santo Thomas JURNAL PENDIDIKAN TAMBUSAI JOURNAL OF SCIENCE AND SOCIAL RESEARCH Jurnal Sains dan Teknologi KAKIFIKOM : Kumpulan Artikel Karya Ilmiah Fakultas Ilmu Komputer Jurnal Sosial dan Teknologi Instal : Jurnal Komputer Jurnal Info Sains : Informatika dan Sains Jurnal Minfo Polgan (JMP) Jurnal Teknik Informatika Unika Santo Thomas (JTIUST) Jurnal Dinamika Informatika (JDI) Jurnal Inkofar sudo Jurnal Teknik Informatika Ulead : Jurnal E-pengabdian Journal of Artificial Intelligence and Digital Business LOFIAN: Jurnal Teknologi Informasi dan Komunikasi Journal of Advanced Computer Knowledge and Algorithms Jurnal Ilmu Komputer dan Informatika Jurnal Teknik Indonesia (JU-TI) Journal of Digital Business and Data Science Jurnal Ilmu Komputer dan Teknik Informatika
Sipayung, Sardo Pardingotan
Universias Katolik Santo Thomas Medan Jl. Setiabudi No. 479 F Tanjungsari Medan
Humanities Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Medicine & Pharmacology Physics Public Health Social Sciences Transportation Other

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Analisis Pertumbuhan Ekonomi Indonesia pada Periode Kebijakan Fiskal (2020–2024) Menggunakan K-Means Clustering Sinaga, Rafael Grealdi; Purba, Marta Rahmawati; Sipayung, Sardo Pardingotan
Jurnal Ilmu Komputer dan Informatika | E-ISSN : 3063-9026 Vol. 2 No. 3 (2026): Januari - Maret
Publisher : GLOBAL SCIENTS PUBLISHER

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

Abstract

Economic growth is one of the main indicators in assessing the success of a country's fiscal policy. In the modern fiscal policy period, Indonesia's economic growth shows different dynamics across regions. This study aims to analyze Indonesia's economic growth patterns using the K-Means Clustering method. The data used in this study are sourced from the Indonesian Regional Data Management Information System (SIMREG) for the 2020–2024 period, with variables including Gross Domestic Product (GDP) growth rate, poverty rate, and poverty rate. The K-Means method is used to group regions based on similar economic growth characteristics. The results show the formation of three main clusters: regions with high, medium, and low economic growth. Each cluster has different economic characteristics, reflecting growth inequality between regions in Indonesia. This research is expected to serve as evaluation material and reference for the government in planning more targeted fiscal policies and encouraging equitable economic growth in Indonesia.
Analisis Sentimen Opini Warga X terhadap Banjir di Sumatera Menggunakan Naive Bayes Frans, Paulina Gorat; Frans Steven Pakpahan; Sardo Pardingotan Sipayung
Jurnal Dinamika Informatika Vol. 15 No. 1 (2026): Vol. 15 No. 1 (2026)
Publisher : Program Studi Informatika Universitas PGRI Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31316/jdi.v15i1.461

Abstract

Penelitian ini bertujuan untuk menganalisis sentimen opini masyarakat terhadap peristiwa banjir di wilayah Sumatera berdasarkan data dari media sosial X. Banjir merupakan bencana alam yang sering terjadi di Sumatera dan menimbulkan berbagai respons masyarakat yang banyak disampaikan melalui media sosial. Media sosial X dipilih sebagai sumber data karena bersifat terbuka dan real-time sehingga dapat merepresentasikan opini publik secara luas. Data penelitian terdiri dari 1.030 tweet berbahasa Indonesia yang dikumpulkan melalui proses crawling menggunakan API resmi X dengan kata kunci terkait banjir di Sumatera. Setelah dilakukan pembersihan data, diperoleh 873 tweet yang kemudian diproses melalui tahapan text mining, meliputi preprocessing teks, pelabelan sentimen secara manual, serta pembagian data menjadi data latih dan data uji. Data latih berjumlah 650 tweet, sedangkan data uji sebanyak 223 tweet. Klasifikasi sentimen dilakukan menggunakan algoritma Naive Bayes dengan bantuan perangkat lunak RapidMiner. Evaluasi model dilakukan menggunakan confusion matrix dengan metrik accuracy, precision, recall, dan F1-score. Hasil penelitian menunjukkan bahwa algoritma Naive Bayes mampu memberikan kinerja yang cukup baik dalam mengklasifikasikan sentimen. Selain itu, hasil analisis menunjukkan bahwa opini masyarakat terhadap peristiwa banjir di wilayah Sumatera didominasi oleh sentimen negatif. Penelitian ini diharapkan dapat memberikan gambaran mengenai persepsi masyarakat dan menjadi bahan pertimbangan dalam pengambilan kebijakan penanggulangan bencana.
Perbandingan Kinerja Naive Bayes dan KNN Dalam Klasifikasi Sentimen Ulasan Film Horor Simbolon, Cantriya; Maria Angelina Lubis; Sardo Pardingotan Sipayung
Jurnal Dinamika Informatika Vol. 15 No. 1 (2026): Vol. 15 No. 1 (2026)
Publisher : Program Studi Informatika Universitas PGRI Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31316/jdi.v15i1.464

Abstract

Lonjakan ulasan film horor di platform digital memerlukan sistem klasifikasi otomatis untuk memahami sentimen penonton secara efisien. Penelitian ini bertujuan membandingkan kinerja algoritma Naive Bayes dan K-Nearest Neighbors (KNN) dalam mengklasifikasikan sentimen ulasan film horor berbahasa Inggris. Metodologi penelitian melibatkan pengolahan 3.000 data dari Kaggle menggunakan perangkat lunak RapidMiner, dengan tahapan pra-pemrosesan meliputi pembobotan TF-IDF, tokenization, filtering, dan stemming. Pengujian dilakukan melalui skema 10-fold cross validation untuk menjamin stabilitas hasil. Temuan penelitian menunjukkan perbedaan performa yang signifikan, di mana Naive Bayes meraih akurasi sebesar 88,53%, jauh mengungguli KNN yang hanya mencapai 40,47%. Rendahnya akurasi KNN disebabkan oleh kompleksitas perhitungan jarak pada data teks berdimensi tinggi. Disimpulkan bahwa Naive Bayes merupakan model yang lebih reliabel dan efektif untuk klasifikasi sentimen ulasan film horor. Hasil ini memberikan kontribusi berupa rekomendasi algoritma optimal bagi pengembangan sistem analisis opini otomatis.
Implementation of Data Mining for Customer Segmentation Using the K Means Clustering Algorithm Based on Annual Income and Spending Score Lumban Gaol, Fortina; Sipayung, Sardo Pardingotan
Journal of Digital Business and Data Science Vol. 3 No. 1 (2026): Journal of Digital Business And Data Science
Publisher : Politeknik Siber Cerdika Internasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59261/jdbs.v3i1.28

Abstract

Background: This research is motivated by the dynamics of the retail industry, which requires a deep understanding of consumer behavior in order to compete effectively in an increasingly competitive market. Many marketing strategies fail to achieve optimal results because they overlook variations in individual shopping behavior within large customer populations. Understanding these behavioral differences is important for developing more targeted and effective marketing strategies. Objective: This study aims to group customers into homogeneous segments in order to support more precise strategic decision-making in marketing activities. Method: The study applies a data mining approach using the K-Means clustering algorithm to analyze a dataset consisting of 200 customers. The clustering process is conducted based on two main variables, namely annual income and spending score, to identify patterns of consumer behavior. Findings and Implications: The results reveal five distinct consumer clusters with different behavioral characteristics. The Target group represents the majority with 81 customers, followed by the Sultan group (39 customers), the Thrifty group (35 customers), the Passive group (23 customers), and the Impulsive group (22 customers). The findings indicate that income level does not always correlate linearly with consumption intensity, implying that behavioral-based segmentation provides more accurate insights for marketing strategy development. Conclusion: Customer segmentation using the K-Means clustering algorithm enables clearer identification of target markets through well-defined cluster separation. Therefore, marketing strategies should emphasize lifestyle orientation rather than focusing solely on purchasing power to optimize customer loyalty and engagement.
Co-Authors Ade Linhar P Alex Rikki Andreas, Kevin Antonius Siagian, Novriadi Baehaqi Barus, Paskalia Br Batubara, Muhammad Iqbal Br Ginting, Anirma Kandida Cristina Situmorang Efendi, Syahril Fernando, Juniko Frans Steven Pakpahan Frans, Paulina Gorat Gaol, Sasmita Lumban Garingging, Cesia Trisani Saragih Ginting, Anirma Ginting, Anirma Kandinda Giovani, Aritonang Girsang, Jahanra Gracia Simatupang Gulo, Jelita Astrid Harianja, Andy Paul Hasugian , Paska Marto Hia, Hikmat Pengertian Hulu, Setiani Hutauruk, Amelia Sanna Maria Lahagu, Marlinus Lahagu, Nicolas Elsada Lase, Marsindra Yanti Limbeng, Yuni br Lubis, Maria Angelina Lumban Gaol, Fortina Lumbanbatu, Noperla Anjelisari LumbanBatu, Vio Br Maha, Yadi Limanta Mahyuddin K. M Nasution Manalu, Ester Manurung, Evaldo Manurung, Saut Maria Angelina Lubis Marmata, Sri Ulina Br Maruwahal Sijabat, Ramson Rikson Matondang, Zekson Aizona Meri Nova Marito Br Sipahutar Naibaho, Marcel Naibaho, Wirma Nainggolan, Kevin Marcho Napitupulu, Virzinia Nunes, Minaldinu Deyesus Panggabean, Jusnan Pasaribu, Adri Purba, Ade Purba, Jhonatan Purba, Marta Rahmawati Rajagukguk, Jonatan Carlos riang, rya Ricardo, Erich Ritonga, Margan Rizkiano Sagala, Lauren Patricia Sagala, Masdiana Saragih, Dea Ananda Sembiiring, Dia Alemisa br Sembiring, Boy Mountavani Sembiring, Brema Aprilta Sembiring, Dessianna Natalia Siagian, Novriadi Antonius Sianturi, Firman Torino SIBURIAN, MANANDA TURE Sihombing, Carlo Poda Boromeo Sihotang, Yuli Pitriani Br Silaban, Daniel Silalahi, Rasit Junaedi Simanjuntak, Richard Parlindungan Simanjuntak, Theresya Simbolon, Agustina Simbolon, Cantriya Simbolon, Daniel S. Simbolon, Yoel Sinaga, Elvis Lavenius sinaga, lotar mateus Sinaga, Rafael Grealdi Sirait, Juan Sebastian Siringoringo, Maysya Faiftin Sitanggang, Armando Agasi Sitanggang, Romualda Sitanggang, Roni Gabe Situmorang, Cristina Situmorang, Yudi Yohannes Sorang Pakpahan Surbakti, Efrans Tambunan, Dwito Julian Tambunan, Yosua Tampubolon, Albert Julio Tampubolon, Amsal Tarigan, Jenheri Rejeki TONNI LIMBONG Tulus Pramita Sihaloho Zakarias Situmorang Zebua, Wilfred Raimond Zekson Matondang