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All Journal JATI (Jurnal Mahasiswa Teknik Informatika) JOURNAL OF INFORMATION SYSTEM MANAGEMENT (JOISM) Fahma : Jurnal Informatika Komputer, Bisnis dan Manajemen
Minarwati
STMIK El Rahma Yogyakarta
Computer Science & IT Decision Sciences, Operations Research & Management Engineering

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IMPLEMENTASI ALGORITMA K-MEANS UNTUK KLASTERISASI TINGKAT KEDISIPLINAN PEGAWAI PADA SISTEM KEPEGAWAIAN STMIK EL RAHMA YOGYAKARTA Ilhan Manzis; Minarwati Minarwati
JATI (Jurnal Mahasiswa Teknik Informatika) Vol. 10 No. 3 (2026): JATI Vol. 10 No. 3
Publisher : Institut Teknologi Nasional Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36040/jati.v10i3.18387

Abstract

Pengelolaan data presensi pada Sistem Kepegawaian STMIK El Rahma Yogyakarta saat ini masih terbatas pada pencatatan administratif sehingga belum mampu menghasilkan informasi analitis terkait kedisiplinan pegawai. Permasalahan tersebut mendorong perlunya pemanfaatan teknik data mining untuk mengelompokkan tingkat kedisiplinan secara objektif. Penelitian ini bertujuan untuk mengimplementasikan algoritma K-Means Clustering dalam mengelompokkan tingkat kedisiplinan pegawai berdasarkan data presensi. Metode yang digunakan mengacu pada tahapan Knowledge Discovery in Databases (KDD), meliputi selection, preprocessing, transformation, data mining, dan evaluation. Data yang diolah berupa persentase pemenuhan jam kerja dan kehadiran selama tiga bulan. Hasil penelitian menunjukkan bahwa data berhasil dikelompokkan menjadi tiga klaster, yaitu kedisiplinan tinggi, sedang, dan rendah. Evaluasi menggunakan Silhouette Score menghasilkan nilai rata-rata sebesar 0,500844 yang menunjukkan kualitas klasterisasi dalam kategori cukup baik. Hasil klasterisasi juga telah diimplementasikan ke dalam sistem sehingga mampu menyajikan informasi kedisiplinan pegawai secara otomatis dan mendukung proses evaluasi kinerja secara lebih efektif. Pengujian sistem menggunakan metode blackbox menunjukkan bahwa seluruh fungsi aplikasi berjalan sesuai dengan yang diharapkan
Analisis Active learning SVM berbasis Margin Sampling pada Sentimen YouTube MBG Minarwati; Alvian Putra Hardiadi
Jurnal Informatika Komputer, Bisnis dan Manajemen Vol 24 No 2 (2026): Mei 2026
Publisher : LPPM STMIK El Rahma Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61805/fahma.v24i2.203

Abstract

The Free Nutritious Meal Program (MBG) has generated extensive public discussion on YouTube, making sentiment analysis a valuable tool for understanding public perceptions. This study investigates the effectiveness of Support Vector Machine (SVM)-based Active Learning (AL) using a Margin Sampling strategy and a Human-in-the-Loop (HITL) framework, in which researchers acted as oracles following annotation guidelines for three sentiment classes: Negative, Neutral, and Positive. The dataset comprised 999 labeled Indonesian YouTube comments, split into 599 initial training samples, 200 oracle pool samples, and 200 fixed test samples. Texts were represented using TF-IDF features with unigrams and bigrams (max_features = 5,000). Three approaches were compared: AL-HITL, Simulated Active Learning, and Random Sampling. After 15 iterations involving 50 additional labeled samples, AL-HITL achieved the highest macro F1-score of 0.5389, outperforming Simulated AL (0.4950) and Random Sampling (0.4977). A skip mechanism with a 33.3% skip rate reduced negative learning, whereas the other approaches experienced performance declines from the baseline score of 0.5154. Positive sentiment, the minority class (20%), yielded the lowest F1-score (0.4286), while the majority negative class (40.7%) achieved the highest (0.6098). These findings provide preliminary empirical evidence on the potential and challenges of Margin Sampling-based Active Learning for Indonesian YouTube sentiment analysis.
Co-Authors Alvian Putra Hardiadi Fitri, Risa Aulia Hasniar Hasniar, Hasniar Herdiesel Santoso Ilhan Manzis Intan Hidayah Kholidun Kholidun Muzakkar, Momon Rahman , Taufik Andri Shimbun, Annisa Fikria Vetliyanti Mavikasari