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Rizal Lamusu
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Computer Science & IT

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PERBANDINGAN ALGORITMA NAÏVE BAYES DAN C4.5 UNTUK KLASIFIKASI BANTUAN RUMAH SEHAT Irawan Ibrahim; Hilmansyah Gani; Rizal Lamusu; Yulan Humolungo
Jurnal Ilmu Komputer (JUIK) Vol 2, No 1 (2022): February 2022
Publisher : Universitas Muhammadiyah Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (822.422 KB) | DOI: 10.31314/juik.v2i1.1477

Abstract

Klasifikasi adalah sebuah teknik untuk menentukan keanggotaan kelompok berdasarkan data-data yang sudah ada. Penentuan pemilihan penerima bantuan rumah sehat hanya di putuskan melalui forum musyawarah desa sehingga membutuhkan ketelitian dalam pemenuhan kriteria dan syarat penerima bantuan rumah sehat. Pada pengumpulan data, data yang didapatkan memiliki 11 kriteria di antaranya yaitu nama kepala keluarga, pekerjaan, usia pernikahan status perkawinan, jumlah anggota keluarga, status kepemilikan tanah, atap, langit-langit, lantai, dinding dan jamban. Tahap pengujian menggunakan 114 record data. Keakurasian dari hasil uji coba menggunakan Algoritma Naïve Bayes dan C4.5 ditinjau dari dua parameter yaitu X-Validation dan jumlah data training. Nilai keakurasian hasil klasifikasi yang ditinjau dari parameter Algoritma C4.5 mendapatkan nilai akurasi yang tertinggi dengan tingkat akurasi 96.51% dibandingkan dengan Algoritma Naïve Bayes dengan tingkat akurasi sebesar 95,61%.
ANALISIS PROSES BISNIS KENAIKAN PANGKAT PADA DINAS PERTANIAN, PERKEBUNAN DAN KETAHANAN PANGAN KABUPATEN BONE BOLANGO Rizal Lamusu
Jurnal Ilmu Komputer (JUIK) Vol 1, No 1 (2021): February 2021
Publisher : Universitas Muhammadiyah Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (536.291 KB) | DOI: 10.31314/juik.v1i1.775

Abstract

In the process of filing the promotion of civil servants at the Office of Argriculture. Plantation and Food Security Bone Bolango regency experienced the problems because the data or file civil servants are not stored in the field officials in Agriculture, Plantation and Food Security, Bone Bolango regency and data collection on the promotion of civil servants still do manually or conventional so takes long time. This research aims to simplify and accelerate the process of conducted with using the Research and Development (R and D) as well as development of systems using Bussiness Process Model Natotation  (BPMN). The result in this reserach showed gaps and opportunities for increase the business process in the Office of Agriculture, Plantation and Food Security in Bone Bolango regenvy that includes major activities and supporting activity. This research can be developed in the form of base web, mobile web, and security system.
PENERAPAN MODEL UNIFIED THEORY OF ACCEPTANCE AND USE OF TECHNOLOGY (UTAUT) TERHADAP PENGGUNAAN SISTEM INFORMASI AKADEMIK UNIVERSITAS MUHAMMADIYAH GORONTALO Silvana Yusuf; Mohamad Ilyas Abas; Syahrial Syahrial; Rizal Lamusu
Jurnal Ilmu Komputer (JUIK) Vol 2, No 2 (2022): October 2022
Publisher : Universitas Muhammadiyah Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31314/juik.v2i2.1714

Abstract

enelitian ini bertujuan untuk menerapkan model UNIFIED THEORY OF ACCEPTANCE AND USE OF TECHNOLOGY (UTAUT) pada Sistem Informasi Akademik (SIA) Universitas Muhammadiyah Gorontalo. Model UTAUT dipilih karena dapat menggambarkan representatif dari keterterimaan sistem dari perilaku pengguna terkait teknologi baru maupun lama. Empat konstruk yang di uji yakni Ekpektasi Kinerja (Performance Expectancy), Ekpektasi Usaha (Effort Expectancy), Pengaruh Sosial (Social Influence), Kondisi Fasilitas (Facility Condition) untuk mengetahui pengaruh Minat Perilaku (Behavioral Intention) dan Perilaku Menggunakan (Use Behavior). Responden yang berhasil dikumpulkan sebanyak 235 yang berasal dari semua program studi baik dosen dan mahasiswa. Data dikumpulkan melalui sebaran kuisioner kemudian diolah menggunakan salah satu aplikasi pengolahan statistik yakni Lisrel. Hasil yang didapatkan dari pengolahan tersebut dari ke empat variabel tersebut berpengaruh positif terhadap Minat Perilaku dan Perilaku Menggunakan sebesar lebih dari 90%. Manfaat dari penelitian dapat memberikan informasi keterterimaan SIA UMGO dikalangan pengguna untuk mengetahui seberapa penting peran SIA UMG dan juga pengembangan sistem kedepan.
APPLE FRUIT QUALITY DETECTION (GOOD AND ROTTEN) USING THE YOLOV5 METHOD Fathir Adisyar; Mohamad Ilyas Abas; Widya Eka Pranata; Rizal Lamusu; Syahrial Syahrial; Irawan Ibrahim
Jurnal Ilmu Komputer (JUIK) Vol 6, No 1 (2026): February 2026
Publisher : Universitas Muhammadiyah Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31314/juik.v6i1.5539

Abstract

Fruit quality is an important factor that affects nutritional value, consumption safety, and market value of agricultural products. Apples, as one of the most widely consumed fruits, are prone to quality degradation due to spoilage, which is often difficult to accurately identify through human visual observation. Manual sorting of apples is subjective, time-consuming, and prone to errors. Therefore, this study aims to develop an automatic apple quality detection and classification system using the You Only Look Once version 5 (YOLOv5) deep learning method. Apple quality is classified into two categories, namely fresh apples and rotten apples, based on digital images. The dataset used in this study consists of 4,035 images obtained from the Roboflow platform, comprising 2,925 training images, 707 validation images, and 403 testing images. All images were resized to 640 × 640 pixels without data augmentation. The model was trained for 50 epochs using GPU acceleration on Google Colab. Model performance was evaluated using a confusion matrix on the testing dataset. The experimental results show that the YOLOv5 model successfully classified all testing images correctly without any misclassification, indicating excellent detection and classification performance. These results demonstrate that YOLOv5 is an effective and reliable method for automatic apple quality detection and has strong potential for application in agriculture and the food industry to improve efficiency and accuracy in fruit quality inspection.
Co-Authors Fathir Adisyar Gani, Hilmansyah Ibrahim, Irawan Mohamad Ilyas Abas Silvana Yusuf Syahrial Syahrial Syahrial Syahrial Widya Eka Pranata Yulan Humolungo