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All Journal Progresif: Jurnal Ilmiah Komputer
Yoakhina Nicole Makaruku
Institut Agama Kristen Negeri Ambon
Computer Science & IT Control & Systems Engineering

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Implementation of MobileNetV2 Transfer Learning for Image-Based Classification of Cocoa Fruit Diseases Yoakhina Nicole Makaruku; Jermias Victor Manuhutu; Jenifer Gabriela Neyte
Progresif: Jurnal Ilmiah Komputer Vol 22, No 2 (2026): April
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35889/progresif.v22i2.3662

Abstract

Cocoa is one of the high-value agricultural commodities in Indonesia; however, its productivity continues to decline due to the increasing prevalence of plant diseases, particularly Black Pod Disease and attacks by Helopeltis spp. Traditional disease detection, which is generally performed manually by farmers, is often inefficient and prone to errors, thereby highlighting the need for an intelligent and technology-assisted early diagnosis system. This study aims to develop a disease classification model for cocoa plants using a Convolutional Neural Network (CNN) based on the MobileNet-V2 architecture, which is recognized for its computational efficiency and strong performance in image analysis. The dataset consisted of 300 images divided into three categories: healthy cocoa pods, Black Pod Disease, and damage caused by Helopeltis. Following the Pareto principle, 80% of the data were allocated for training and 20% for testing. The model was trained for 10 epochs with a batch size of 32 and was supported by data augmentation to improve data variability. Experimental results demonstrated a significant improvement in performance, with the highest validation accuracy of 93.75% achieved at the seventh epoch. The confusion matrix further confirmed that the model classified each category with a high level of precision. These findings indicate that MobileNet-V2 is an effective approach for automatic cocoa disease detection and has strong potential to assist farmers in improving disease management practices in the field.Key words: Convolutional Neural Network; MobileNet-V2; Cocoa Disease Classification; Helopeltis spp.; Deep Learning 
Analisis dan Visualisasi Tingkat Kelulusan Mahasiswa Menggunakan Algoritma K-Means Clustering dan Silhoutte Score Berdasarkan Asal Daerah Jermias Victor Manuhutu; Yoakhina Nicole Makaruku; Daniel Halauwet
Progresif: Jurnal Ilmiah Komputer Vol 22, No 2 (2026): April
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35889/progresif.v22i2.3663

Abstract

Academic achievement of graduates serves as a key indicator of higher education quality and graduates’ readiness to enter the workforce. This study examines the Christian Religious Education Study Program at the State Christian Institute of Ambon (IAKN Ambon) during the 2010–2020 period, focusing on the influence of students’ district of origin on graduation rates and Grade Point Average (GPA). Employing a descriptive quantitative approach based on secondary data, the analysis was conducted using descriptive statistics and the K-Means clustering method, with the optimal number of clusters evaluated through the silhouette score. The findings reveal a concentration of students from several major districts, including Central Maluku, Ambon City, and West Seram Regency. Four performance clusters were identified: high-performing, moderate-performing, challenging, and low-performing/no graduates. An Average Silhouette Score of 0.77 indicates good cluster quality and strong separation among groups. The study highlights disparities in academic achievement based on students’ geographical origins, suggesting the need for more targeted interventions and stronger collaboration with students’ home districts to improve academic success and graduation outcomes.Keywords: Academic Performance; District of Origin; Graduation Rate; K-Means Clustering; Silhouette ScoreAbstrakPrestasi akademik lulusan menjadi indikator utama mutu pendidikan tinggi dan kesiapan lulusan menghadapi dunia kerja. Penelitian ini mengkaji Program Studi Pendidikan Agama Kristen (PAK) Institut Agama Kristen Negeri Ambon periode 2010–2020, dengan fokus pada pengaruh asal kabupaten terhadap tingkat kelulusan dan IPK. Menggunakan pendekatan kuantitatif deskriptif berbasis data sekunder, analisis dilakukan melalui statistik deskriptif dan metode                K-Means clustering, dengan evaluasi jumlah klaster menggunakan silhouette score. Hasil menunjukkan konsentrasi mahasiswa pada beberapa kabupaten utama seperti Maluku Tengah, Kota Ambon, dan Seram Bagian Barat. Ditemukan empat klaster kinerja, yaitu tinggi, moderat, menantang, dan rendah/tanpa lulusan. Nilai Average Silhouette Score sebesar 0,77 menunjukkan kualitas klaster yang baik. Studi ini mengungkap adanya perbedaan capaian akademik berdasarkan asal daerah, sehingga diperlukan intervensi yang lebih terarah serta kerja sama dengan daerah asal mahasiswa untuk meningkatkan keberhasilan studi.Kata kunci: Asal Kabupaten; Clustering: K-Means; Silhoutte Score; Tingkat Kelulusan
Co-Authors Daniel Halauwet Jenifer Gabriela Neyte Jermias Victor Manuhutu