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All Journal JSAI (Journal Scientific and Applied Informatics) Jurnal Sistem Komputer dan Informatika (JSON)
Andi Mulawati Mas Pratama
Universitas Ichsan Gorontalo Utara
Computer Science & IT Control & Systems Engineering

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Implementasi Pendekatan Myers-Briggs Type Indicator Dalam Sistem Informasi Bimbingan Konseling Berbasis Android Untuk Sekolah Menengah Atas Andi Mulawati Mas Pratama; Misrawati Aprilyana Puspa; Herlianti Nusi
JSAI (Journal Scientific and Applied Informatics) Vol 8 No 2 (2025): Juni
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v8i2.8291

Abstract

Guidance and counseling services at SMA Negeri 3 Gorontalo Utara play a crucial role in helping students recognize their potential, interests, and the problems they face. This study aims to develop an Android-based guidance and counseling information system by integrating the Myers-Briggs Type Indicator (MBTI) approach as a tool for identifying students' personality types. The system is designed to present the MBTI questionnaire, automatically analyze the results, and provide counseling recommendations that correspond to each student's personality type. Testing was conducted on 60 eleventh-grade students at SMA Negeri 3 Gorontalo Utara. The results show that the system is capable of effectively classifying personality types and delivering relevant recommendations. The most commonly found personality types were INFP, ESTJ, and ISFJ. Both students and guidance counselors responded positively to the system's usability, ease of use, and clarity. Although limitations remain in terms of advanced personalization and data integration, the approach has proven to enhance the quality of counseling services in a more adaptive and technology-based manner. This study recommends further feature development and system integration into the school's academic management
Komparasi Algoritma Machine Learning untuk Prediksi Risiko Stunting Berbasis Data P3KE Provinsi Gorontalo Siti Andini Utiarahman; Andi Mulawati Mas Pratama; Satriadi D. Ali
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 7 No. 4 (2026): Juni 2026
Publisher : Universitas Budi Darma

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

Abstract

Stunting merupakan permasalahan kesehatan masyarakat yang serius di Provinsi Gorontalo dengan prevalensi mencapai 23,8% pada tahun 2024, masih jauh di atas target nasional sebesar 14%. Penelitian ini bertujuan mengembangkan model prediksi risiko Stunting menggunakan pendekatan Machine learning berbasis Data Pensasaran Percepatan Penghapusan Kemiskinan Ekstrem (P3KE). Dataset yang digunakan mencakup 87.902 rumah tangga di Provinsi Gorontalo. Lima algoritma dibandingkan, yaitu Decision Tree, K-Nearest Neighbors, Support Vector Machine, Random Forest, dan XGBoost. Evaluasi dilakukan pada dua skenario, yaitu tanpa dan dengan penerapan Synthetic Minority Oversampling Technique (SMOTE). Optimasi hyperparameter dilakukan menggunakan RandomizedSearchCV dengan 3-fold cross-validation. Hasil eksperimen menunjukkan bahwa XGBoost tanpa SMOTE merupakan model terbaik dengan F1-score  sebesar 90,39%, accuracy 90,48%, precision 99,91%, recall 82,52%, dan AUC-ROC 94,09%. Penerapan SMOTE tidak memberikan peningkatan kinerja yang signifikan, dengan selisih F1-score  yang hanya mencapai 0,0067 poin. Sebaliknya, teknik tersebut meningkatkan waktu komputasi hingga 65%. Temuan ini mengonfirmasi bahwa distribusi kelas yang relatif seimbang, yaitu 54,2% : 45,8%, tidak memerlukan proses oversampling. Analisis SHAP mengidentifikasi BAB: Milik sendiri dan Indeks Sanitasi sebagai fitur yang paling berpengaruh dalam prediksi risiko Stunting. Hasil penelitian menunjukkan bahwa data registrasi sosial P3KE tanpa melibatkan data antropometri tetap mampu menghasilkan model prediksi risiko Stunting dengan kinerja yang kompetitif, yang ditunjukkan oleh nilai F1-score  di atas 90%.
Co-Authors Herlianti Nusi Misrawati Aprilyana Puspa Satriadi D. Ali Utiarahman, Siti Andini