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Fadhliani, Fadhliani
Faculty Of Engineering, Universitas Malikussaleh
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Naive Bayes Classification Model Analysis of Livable Housing Model Based on Physical Characteristics Burhanuddin Burhanuddin; Emi Maulani; Syarifah Asria Nanda; Cut Agusniar; Fadhliani Fadhliani
Sisfo: Jurnal Ilmiah Sistem Informasi Vol. 10 No. 1 (2026): Sisfo: Jurnal Ilmiah Sistem Informasi, Mei 2026
Publisher : Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/sisfo.v10i1.26974

Abstract

Analysis of Naive Bayes Classification Model of Livable Housing Model Based on Physical Characteristics is one of the crucial aspects in improving the quality of life and welfare of the community. This study aims to examine the application of the Naive Bayes classification method in determining the level of livability based on the physical characteristics of the building. The dataset used is house data that includes several variables, namely roof condition (good, damaged), wall type (wall, semi-permanent, wood), floor condition (ceramic, cement, soil), building area (<36 m², ≥36 m²), ventilation (adequate, inadequate), and sanitation access (adequate, inadequate). The target variable in this study is the housing category, namely livable and uninhabitable. The research stages include data collection, data preprocessing, dividing the dataset into training data and test data, and implementation of the Naive Bayes algorithm. The posterior probability calculation is carried out based on the probability distribution of each variable against the class with a maximum likelihood approach. Model performance evaluation is carried out using a confusion matrix with indicators of accuracy, precision, and recall. The results of the analysis show that the variables of floor condition and sanitation access have the highest probability value against the livable category, thus playing a dominant role in the classification process. Furthermore, these two variables were also shown to have the most significant influence in determining whether housing is habitable or uninhabitable.
Analysis of Machine Learning-Based Classification Models for Determining Fertilizer Types for Rice Crop Growth: Machine Learning Approach for Optimizing Fertilizer Selection in Rice Cultivation Mira Humaira; Almuna Ramadhani; Uchti Nuzul Qhinanti Lubis; Fadhliani Fadhliani; Septiarini Zuliati; Usnawiyah Usnawiyah
Sisfo: Jurnal Ilmiah Sistem Informasi Vol. 10 No. 1 (2026): Sisfo: Jurnal Ilmiah Sistem Informasi, Mei 2026
Publisher : Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/sisfo.v10i1.27283

Abstract

Determining the appropriate fertilizer type is essential for supporting rice plant growth and optimizing agricultural productivity. However, conventional fertilization practices still rely heavily on empirical judgment and often neglect dynamic soil and plant growth characteristics. This study aims to analyze and compare the performance of several machine learning classification models for fertilizer type determination in rice cultivation. The study employed a computational experimental approach adapted from the CRISP-DM framework using a dataset of 480 records consisting of soil and rice growth parameters, including Nitrogen (N), Phosphorus (P), Potassium (K), soil pH, moisture, and plant height. Five classification algorithms were evaluated, namely Naïve Bayes, K-Nearest Neighbor (KNN), Decision Tree, Support Vector Machine (SVM), and Random Forest. Model performance was assessed using accuracy, precision, recall, and F1-score, combined with Stratified k-Fold Cross Validation. The results showed that Random Forest achieved the best performance with an accuracy of 95.83%, precision of 95.54%, recall of 95.12%, and F1-score of 95.33%. These findings indicate that ensemble learning methods are more effective in handling heterogeneous and multivariable agricultural data than conventional classification approaches. This study contributes to the development of machine learning-based classification analysis for more accurate and data-driven fertilizer determination in rice cultivation.
Pendampingan Pembibitan dan Pemeliharaan Tanaman Kakao Pada Masyarakat Gampong Paya Gaboh Kecamatan Sawang Hafifah Hafifah; Nasruddin Nasruddin; Lukman Lukman; Fadhliani Fadhliani; Septiarini Zuliati; Uchti Nuzul Qhinanti Lubis; Mukhaiyar Azhari; Mahlil Mahlil
Jurnal Vokasi Vol 10, No 1 (2026): Maret
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/vokasi.v10i1.8299

Abstract

Kakao (Theobroma cacao) merupakan tanaman perkebunan bernilai ekonomi tinggi yang berperan sebagai bahan baku utama dalam industri cokelat. Di Indonesia, termasuk Kabupaten Aceh Utara, potensi pengembangan kakao tergolong besar. Namun, produktivitasnya masih rendah akibat terbatasnya bibit unggul yang tersedia. Untuk mengatasi permasalahan ini, telah dilaksanakan suatu kegiatan peningkatan kapasitas dalam teknik pembibitan kakao yang sesuai standar. Kegiatan dilaksanakan di Gampong Paya Gaboh, Kecamatan Sawang, Kabupaten Aceh Utara, pada Agustus hingga Oktober 2025. Metode yang diterapkan meliputi penyuluhan, pelatihan, demonstrasi, serta pendampingan teknis secara berkala. Sebelum pelaksanaan, dilakukan survei lokasi dan sosialisasi kepada masyarakat dan perangkat desa setempat. Materi pelatihan mencakup pemilihan biji, teknik persemaian, pembuatan media tanam, teknik sambung pucuk, dan pengendalian hama penyakit. Bahan praktik seperti media tanam, bibit kakao, serta perlengkapan sambung pucuk disiapkan untuk mendukung kegiatan. Hasil pelaksanaan menunjukkan antusiasme tinggi dari peserta dan minimnya pengetahuan awal terkait teknologi pembibitan kakao yang memenuhi standar mutu. Diharapkan, keterampilan teknis yang diperoleh dapat dimanfaatkan untuk menghasilkan bibit kakao berkualitas secara mandiri, guna meningkatkan produktivitas kebun dan mendukung pembangunan pertanian yang berkelanjutan
Co-Authors Ade Firmansyah Tanjung Adhan Palaguna Akram, Rizalul Almuna Ramadhani Ananda Faridhatul Ulva Ananta, Auliyah Balqis Andriani, Dela Anis Nugrahawati Azra, Dio Syahlung Budi, Rama Burhanuddin Burhanuddin Burhanuddin Burhanuddin Cut Agusniar Cut Rozana Sari Diana Khairani Sofyan Dwitya, Yonadiah Emi Maulani Ersa, Nanda Savira Fadhly, T. Andi Faisal, Rahmad Fazriah Tambunan, Eri Fitriani Fitriani Hafifah Hafifah Hafifah, Hafifah Hanim, Wirda Ismida, Yulina Khaidir Khaidir Khairullah, K Laila Nazirah Lukman Lukman M Sayuti M. Faisi Ikhwali Mahlil Mahlil Mandasari, Oppy Mardina, Vivi Mira Humaira Muhammad Afdal Mukhaiyar Azhari Mukti Qamal Muthmainnah Muthmainnah Nanda Savira Ersa Nanda, Novianda Nanda Nasruddin Navia, Zidni Ilman Nura Usrina Nurdin, Muhammad Yusuf Nurhasanah Nurhasanah Putri, Batari Putri, Kartika Aprilia Rafli, Muhammad Ramadhani, Almuna Ratna Lestari Rishaq, Alief Muhammad Rizky Putra Fhonna S, Rahmansyah Safrizal Safrizal Said Jalalul Akbar Sakral Hasby Puarada Sara Gustia Wibowo Savira Ersa, Nanda Septiarini Zuliati Siregar, Sri Rahayu Br Susilo, Dimas Syahri Ramadhan, Syahri Syarifah Asria Nanda Syarifah Zahra Syibral Malasyi, Syibral Teuku Ilhami Surya Akbar Teuku Mudi Hafli Teuku Muhammad Ridwan, Teuku Muhammad Tisna Harmawan, Tisna Tsunami, Lindu Uchti Nuzul Qhinanti Lubis Usnawiyah Usnawiyah, Usnawiyah Yovi Chandra Yulisda, Desvina Yulius Rief Alkhaly Zurrahmi Wirda