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All Journal 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 Instal : Jurnal Komputer Jurnal Info Sains : Informatika dan Sains Jurnal Minfo Polgan (JMP) Jurnal Teknik Informatika Unika Santo Thomas (JTIUST) 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) Jurnal Ilmu Komputer dan Teknik Informatika
Sipayung, Sardo Pardingotan
Universias Katolik Santo Thomas Medan Jl. Setiabudi No. 479 F Tanjungsari Medan
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 Industrial & Manufacturing Engineering Library & Information Science Mathematics Medicine & Pharmacology Physics Public Health Social Sciences Transportation Other

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OPTIMALISASI VALIDITAS KLASTERISASI IPM MELALUI PENERAPAN VARIASI DISTANCE MEASURE PADA ALGORITMA K-MEANS++ Sipayung, Sardo Pardingotan; Efendi, Syahril
JOURNAL OF SCIENCE AND SOCIAL RESEARCH Vol 8, No 4 (2025): November 2025
Publisher : Smart Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54314/jssr.v8i4.5420

Abstract

Abstract: The Human Development Index (HDI) is an important indicator for measuring the quality of regional development through the dimensions of health, education, and decent living standards. In North Sumatra Province, HDI achievements between districts/cities still show significant disparities, requiring a data-based analytical approach to map development patterns objectively. This study aims to optimize the validity of regional HDI clustering through the application of the K-Means++ algorithm with distance measure variations. This study uses a quantitative approach with an unsupervised learning method. The data analyzed includes HDI, Average Length of Schooling (ALS), and Adjusted Per Capita Expenditure sourced from the Central Statistics Agency. The research stages include data preprocessing and standardization, determining the optimal number of clusters using the Elbow method, applying the K-Means++ algorithm, and evaluating cluster quality using the Davies–Bouldin Index (DBI) and Purity Index. In addition, a comparison of clustering performance based on Euclidean, Manhattan, and Cosine distances was conducted. The results of the study show that the optimal number of clusters is three clusters representing high, medium, and low levels of human development. A DBI value of 0.60 and a Purity Index of 0.61 indicate good clustering quality. Euclidean and Manhattan distances produced the best performance compared to Cosine distance. Keyword: Human Development Index; K-Means++; Clustering; Distance Measure; Davies–Bouldin Index; Purity Index. Abstrak: Indeks Pembangunan Manusia (IPM) merupakan indikator penting untuk mengukur kualitas pembangunan wilayah melalui dimensi kesehatan, pendidikan, dan standar hidup layak. Di Provinsi Sumatera Utara, capaian IPM antar kabupaten/kota masih menunjukkan ketimpangan yang cukup signifikan, sehingga diperlukan pendekatan analitis berbasis data untuk memetakan pola pembangunan secara objektif. Penelitian ini bertujuan untuk mengoptimalkan validitas klasterisasi IPM wilayah melalui penerapan algoritma K-Means++ dengan variasi distance measure. Penelitian ini menggunakan pendekatan kuantitatif dengan metode unsupervised learning. Data yang dianalisis meliputi IPM, Rata Lama Sekolah (RLS), dan Pengeluaran per Kapita Disesuaikan yang bersumber dari Badan Pusat Statistik. Tahapan penelitian mencakup praproses dan standarisasi data, penentuan jumlah klaster optimal menggunakan metode Elbow, penerapan algoritma K-Means++, serta evaluasi kualitas klaster menggunakan Davies–Bouldin Index (DBI) dan Purity Index. Selain itu, dilakukan perbandingan kinerja klasterisasi berdasarkan Euclidean, Manhattan, dan Cosine distance. Hasil penelitian menunjukkan bahwa jumlah klaster optimal adalah tiga klaster yang merepresentasikan tingkat pembangunan manusia tinggi, menengah, dan rendah. Nilai DBI sebesar 0,60 dan Purity Index sebesar 0,61 menunjukkan kualitas klasterisasi yang baik. Euclidean dan Manhattan distance menghasilkan performa terbaik dibandingkan Cosine distance. Kata kunci: Indeks Pembangunan Manusia; K-Means++; Klasterisasi; Distance Measure; Davies–Bouldin Index; Purity Index.
ANALISIS PENGELOMPOKAN KARAKTERISTIK SISWA MENGGUNAKAN METODE K-MEANS DALAM PERSPEKTIF FILSAFAT SAINS KOMPUTER Sipayung, Sardo Pardingotan; Nasution, Mahyuddin K. M.
JOURNAL OF SCIENCE AND SOCIAL RESEARCH Vol 8, No 4 (2025): November 2025
Publisher : Smart Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54314/jssr.v8i4.5153

Abstract

Abstract: The development of information technology in education is changing the way students construct and access knowledge, but differences in academic ability, motivation, discipline, and digital literacy often lead to learning disparities. This study grouped student characteristics using K Means Clustering and reviewed them from the perspective of computer science philosophy: ontology, epistemology, axiology, logic, and induction. The data from 120 students included academic scores, learning motivation, discipline, and digital literacy. After normalization, the number of clusters was determined using the Elbow and Silhouette methods, and the quality of the clusters was evaluated using the Davies–Bouldin Index. The findings indicate an optimal number of three clusters, with a Silhouette value of 0.466 and a DBI of 0.733, indicating fairly good and stable clustering. The three clusters describe: 1) highly motivated students with high digital literacy; 2) disciplined students with good academic performance but moderate digital skills; 3) low-motivated students with low digital literacy who require a personalized and empathetic learning approach. Ontologically, data is not just numbers, but the manifestation of students' digital existence in the modern learning space. Epistemologically, knowledge is formed inductively from students' interactions with technology and data. Axiologically, the clustering results support fairness in digital learning with an approach tailored to student characteristics. The dimensions of logic and induction show the clustering process as a scientific pattern of thinking from observation to meaningful rational generalization. The findings support a balance between algorithmic rationality and human values in digital education. Keyword: K-Means Clustering; Philosophy of Computer Science; Ontology, Epistemology; Axiology; Student Characteristics; Digital Learning. Abstrak: Perkembangan teknologi informasi di pendidikan mengubah cara siswa membangun dan mengakses pengetahuan, tetapi perbedaan kemampuan akademik, motivasi, kedisiplinan, dan literasi digital sering menimbulkan ketimpangan pembelajaran. Penelitian ini mengelompokkan karakteristik siswa dengan K Means Clustering dan meninjaunya melalui perspektif filsafat sains komputer: ontologi, epistemologi, aksiologi, logika, dan induksi. Data 120 siswa meliputi nilai akademik, motivasi belajar, kedisiplinan, dan literasi digital. Setelah normalisasi, jumlah klaster ditentukan lewat metode Elbow dan Silhouette, lalu kualitas klaster dievaluasi dengan Davies–Bouldin Index. Temuan menunjukkan jumlah klaster optimal tiga, dengan nilai Silhouette 0,466 dan DBI 0,733, mengindikasikan pengelompokan yang cukup baik dan stabil. Tiga klaster menggambarkan: 1) siswa bermotivasi dan berliterasi digital tinggi; 2) siswa disiplin dan berprestasi akademik baik, namun kemampuan digital sedang; 3) siswa bermotivasi dan literasi digital rendah yang memerlukan pendekatan pembelajaran personal dan empatik. Secara ontologis, data tidak sekadar angka, melainkan wujud eksistensi digital siswa dalam ruang belajar modern. Epistemologis, pengetahuan terbentuk secara induktif dari interaksi siswa dengan teknologi dan data. Aksiologis, hasil klasterisasi mendukung keadilan pembelajaran digital dengan pendekatan sesuai karakteristik siswa. Dimensi logika dan induksi menunjukkan proses klasterisasi sebagai pola berpikir ilmiah dari observasi menuju generalisasi rasional bermakna. Temuan mendukung keseimbangan antara rasionalitas algoritmik dan nilai kemanusiaan dalam pendidikan digital. Kata kunci: K-Means Clustering; Filsafat Sains Komputer; Ontologi, Epistemologi; Aksiologi; Karakteristik Siswa; Pembelajaran Digital.
Co-Authors Ade Linhar P Alex Rikki Andreas, Kevin Antonius Siagian, Novriadi Baehaqi Barus, Paskalia Br Batubara, Muhammad Iqbal Br Ginting, Anirma Kandida Efendi, Syahril Fernando, Juniko Gaol, Sasmita Lumban Garingging, Cesia Trisani Saragih Ginting, Anirma Ginting, Anirma Kandinda Giovani, Aritonang Girsang, Jahanra Gulo, Jelita Astrid Harianja, Andy Paul Hasugian , Paska Marto Hia, Hikmat Pengertian Hulu, Setiani Lahagu, Marlinus Lahagu, Nicolas Elsada Limbeng, Yuni br Lubis, Maria Angelina Lumbanbatu, Noperla Anjelisari Maha, Yadi Limanta Mahyuddin K. M Nasution Manalu, Ester Manurung, Saut Maruwahal Sijabat, Ramson Rikson Matondang, Zekson Aizona Meri Nova Marito Br Sipahutar Naibaho, Marcel Naibaho, Wirma Nainggolan, Kevin Marcho Nunes, Minaldinu Deyesus Panggabean, Jusnan Pasaribu, Adri Purba, Ade Purba, Jhonatan Rajagukguk, Jonatan Carlos riang, rya Ricardo, Erich 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 Silalahi, Rasit Junaedi Simanjuntak, Richard Parlindungan Simanjuntak, Theresya Simbolon, Daniel S. Simbolon, Yoel Sinaga, Elvis Lavenius sinaga, lotar mateus Sirait, Juan Sebastian Sitanggang, Roni Gabe Situmorang, Yudi Yohannes Sorang Pakpahan Surbakti, Efrans Tambunan, Yosua Tampubolon, Albert Julio Tampubolon, Amsal Tarigan, Jenheri Rejeki TONNI LIMBONG Tulus Pramita Sihaloho Zakarias Situmorang Zekson Matondang