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All Journal (JELIKU) Jurnal Elektronik Ilmu Komputer Udayana JURNAL ILMIAH MAHASISWA AGROINFO GALUH Journal of Artificial Intelligence and Engineering Applications (JAIEA) Jurnal Ilmiah Hubungan Internasional Fajar
Dede Rohman
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Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Languange, Linguistic, Communication & Media Social Sciences Other

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STRUKTUR DAN DISTRIBUSI PENDAPATAN PETANI PENANGKAR TEH (Camellia sinensis L. O. Kuntze) GAMBUNG 7 DI DESA CUKANGKAWUNG KECAMATAN SODONGHILIR KABUPATEN TASIKMALAYA Dede Rohman; Agus Yuniawan Isyanto; Muhamad Nurdin Yusuf
Jurnal Ilmiah Mahasiswa Agroinfo Galuh Vol 7, No 3 (2020): September 2020
Publisher : Universitas Galuh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25157/jimag.v7i3.4201

Abstract

Penelitian ini bertujuan untuk mengetahui: 1). pendapatan petani penangkar teh; 2). struktur pendapatan; dan 3). distribusi pendapatan petani penangkar teh Gambung 7 di Desa Cukangkawung Kecamatan Sodonghilir Kabupaten Tasikmalaya. Jenis penelitian ini adalah penelitian kualitatif dengan metode studi kasus. Pengambilan responden dilakukan dengan sensus kepada 10 petani penangkar teh. Data primer dan data sekunder dikumpulkan melalaui observasi, wawancara, studi literatur dan dokumentasi. Pendapatan petani dianalissi dengan analisis biaya, penerimaan dan pendapatan serta kelayakan usaha dianalisis menggunakan R/C ratio. Struktur pendapatan dianalisis dengan analisis struktur pendapatan dan distribusi pendapatan dengan Indeks Gini Ratio. Hasil penelitian menunjukkan bahwa 1) Pendapatan rata-rata petani penangkar teh Gambung 7 sebesar Rp. 214.484.104,- per tahun yang bersal dari penerimaan sebesar Rp. 529.607.250 dan biaya total Rp. 352.598.146,- dengan R/C ratio sebesar 1,53; 2). Struktur pendapatan petani berasal dari kontribusi sektor pertanian 69,36 % yang berasal dari usaha penangkaran teh 43,91 %, sektor non budiaya 14,28 % dan sektor usahatani non penangkar 11,18 % dan dari sektor non pertanian sebesar 30,64 persen; dan 3). Distribusi pendapatan petani penangkar teh di Desa Cukangkawung Kecamatan Sodonghilir menunjukkan ketimpangan rendah dengan nilai Indeks Gini Ratio sebesar 0,260.
Implementasi Support Vector Regression Untuk Prediksi Harga Rumah Dengan Optimasi Grid Search Mulyawan .; Reza Subagja; Dede Rohman; Deny Indriyana Efendi
JELIKU (Jurnal Elektronik Ilmu Komputer Udayana) Vol 12 No 3 (2024): JELIKU Volume 12 No 3, February 2024
Publisher : Informatics Department, Faculty of Mathematics and Natural Sciences, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/JLK.2023.v12.i03.p24

Abstract

Rumah merupakan kebutuhan pokok dalam kehidupan manusia, berfungsi sebagai tempat perlindungan tidak hanya dari kondisi cuaca eksternal, tetapi juga dari makhluk hidup lainnya. Harga rumah menjadi elemen kunci dalam transaksi properti, baik melalui cara konvensional maupun digital. Penelitian ini memiliki tujuan untuk menerapkan algoritma Support Vector Regression (SVR) dalam konteks prediksi harga rumah berdasarkan karakteristiknya. Dalam upaya mencapai kinerja model yang optimal, dilakukan optimasi parameter menggunakan algoritma Grid Search. Data yang digunakan diperoleh melalui teknik web scraping dari situs properti rumah123.com, dengan fokus pada wilayah Jakarta. Atribut data mencakup lokasi, luas tanah, luas bangunan, jumlah kamar tidur, jumlah kamar mandi, kapasitas garasi, dan harga rumah. Metode penelitian melibatkan langkah-langkah Obtain, Scrub, Explore, Model, dan Interpret. Dalam pemodelan, dua skenario data dieksplorasi, yakni menggunakan dataset asli dan hasil transformasi logaritma pada variabel target. Hasil penelitian menunjukkan bahwa model terbaik ditemukan pada skenario data hasil transformasi logaritma, dengan nilai metrik RMSE = 0.2774, MAE = 0.2061, MAPE = 0.1453, dan R-Squared = 0.7867. Parameter optimal yang dihasilkan dari metode grid search adalah Cost = 1, epsilon = 0.1, dan gamma = 1. Keywords: Prediksi Harga Rumah, Support Vector Regression, Grid Search
PARADIPLOMASI KOTA BANDUNG DALAM PERJANJIAN MILAN URBAN FOOD POLICY PACT (MUFPP) UNTUK PENGEMBANGAN SISTEM PANGAN BERKELANJUTAN Dede Rohman; Kardina; Sulastri
Jurnal Ilmiah Hubungan Internasional Fajar Vol. 2 No. 2 (2024): Edisi Juni
Publisher : Prodi Ilmu Hubungan Internasional, Universitas Fajar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47354/jihif.v2i2.787

Abstract

This research aims to explain the implementation of policy and framework of the Milan Urban Food Policy Pact (MUFPP) with the Bandung City Government in the sustainable food system program. This research uses the concept of paradiplomacy and International Regime theory. The results of the research explain that the implementation of the MUFPP policy and framework is proceeding in accordance with the initial objectives of the City of Bandung for joining, namely the development of a sustainable food system program in three (3) policy categories and a 12-point framework or action recommendations that have been implemented by the City of Bandung. This global paradiplomacy has provided effective results by fulfilling the goal of a sustainable food system from the independence of the city of Bandung to reducing its food dependence.
Naive Bayes Algorithm to Enhance Sentiment Analysis of Coursera Application Reviews on Google Play Store Masdarul Rizqi; Martanto; Arif Rinaldi Dikanda; Dede Rohman
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 4 No. 2 (2025): February 2025
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v4i2.758

Abstract

Coursera is an online learning platform that provides various courses and certifications. This study aims to analyze user perceptions of the Coursera application after the reviews are translated into Indonesian, identify factors that influence positive and negative sentiment, and activate the effectiveness of the Naive Bayes algorithm in classifying review sentiment. The method used is Knowledge Discovery in Databases (KDD), with stages of data collection, preprocessing, and sentiment analysis using Naive Bayes. The results of the study show that the translation of reviews does not change the essence of user perception. Analysis of key words reveals positive experiences such as "kursus", "berguna", and "terima kasih", as well as criticism related to application performance. Factors such as price, content, and user experience play an important role in positive sentiment, while technical issues are the main cause of negative sentiment. The Naive Bayes model shows high accuracy with an accuracy value of 83.62%, precision of 83.34%, recall of 87%, and F1-score of 85.2%. These results indicate that the Naive Bayes algorithm is effective in analyzing sentiment of Coursera application user reviews. Further research is recommended to explore other algorithms or expand the analysis by considering additional factors that can influence user sentiment
Improvement of Students' Academic Achievement Classification Model Through the Analytical Hierarchy Process Algorithm in Elementary School Burujul Kulon III Candra, Candra Rahmawati; Martanto; Arif Rinaldi Dikananda; Dede Rohman
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 4 No. 2 (2025): February 2025
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v4i2.764

Abstract

This study aims to improve the classification model of students' academic achievement at SD Negeri Burujul Kulon III using the Analytical Hierarchy Process (AHP) algorithm. This method is applied to analyze various student assessment criteria, including knowledge, skills, spiritual attitudes, and social attitudes, in order to create an objective and systematic evaluation system. The Knowledge Discovery in Databases (KDD) approach is used to ensure structured data management, including the stages of data collection, selection, transformation, analysis, and evaluation. The population of this study was all students of SD Negeri Burujul Kulon III with a sample size taken using the Stratified Random Sampling method, which ensures accurate representation of all grade levels. Data were collected through documentation studies, including exam result reports, skill assessments, and student behavior records observed by teachers. The analysis was carried out by applying the AHP algorithm to determine the priority weight of each assessment criterion through pairwise comparisons. The weights obtained are used to calculate the final grade which is the basis for classifying student achievement. The results of the study indicate that the AHP algorithm is able to produce a more accurate and relevant classification model to identify students with superior achievement, which is not only based on academic exam results but also includes student skills and attitudes. The resulting system provides significant benefits in academic decision-making, such as awarding outstanding students, identifying students who need special attention, and developing more effective learning strategies. This research also contributes to the development of data-based technology for educational evaluation, and can be an important reference for other educational institutions that want to improve the quality of evaluation, learning effectiveness, and student data management comprehensively, systematically, and sustainably in the future.
Use of K-Means Algorithm in Model Improvement Production Data Grouping for Determination Convection Production Strategy Ica, Ica Pandia; Martanto; Arif Rinaldi Dikananda; Dede Rohman
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 4 No. 2 (2025): February 2025
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v4i2.775

Abstract

This research was conducted to implement the K-Means Clustering algorithm in grouping convection production data to support the improvement of efficiency and effectiveness of production strategies. The data used is PT ABC's production data, which consists of important attributes, such as Production ID, Production Date, Product Name, Product Type, Color, Size, Raw Materials, and Order. The research method follows the stages of Knowledge Discovery in Database (KDD), which includes selection, preprocessing, transformation, data mining, and evaluation, so as to ensure that the data processed is relevant and ready to be analyzed. The grouping process is carried out using the K-Means algorithm, which groups data based on attribute similarity by determining the optimal number of clusters. The evaluation of the clustering results was carried out using the Silhouette Score and Davies-Bouldin Index metrics, where the results showed values that represented good cluster quality. A high Silhouette Score indicates that the data in the cluster has good uniformity, while a low Davies-Bouldin Index indicates a clear distance between clusters. The results of the grouping produce three main clusters that illustrate different production patterns, such as clusters with high, medium, and low order quantities. This analysis of the cluster provides important insights in supporting strategic decision-making, such as prioritizing resource management in high-order clusters and evaluating production efficiency in low-order clusters. This research is in line with previous literature that shows that the K-Means algorithm can be used effectively in big data grouping to support strategic planning. The practical contribution of this research is to help convection companies in understanding production patterns, so that production strategies can be designed more efficiently, responsively, and directionally. For further research, it is recommended to add new variables, such as production costs or work duration, as well as test other clustering algorithms to obtain more comprehensive results.
Sales Data Analysis using Linear Regression Algorithm on Raw Water Sales Rohayati, Eti; Martanto; Arif Rinaldi Dikananda; Dede Rohman
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 4 No. 2 (2025): February 2025
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v4i2.809

Abstract

This study aims to assess the effectiveness of linear regression algorithm in predicting raw water demand by considering customer transaction data, raw water volume, and seasonal variables. The method used is Knowledge Discovery in Databases (KDD), including data selection, preprocessing, transformation, data mining, and result evaluation. The dataset is divided 80% for training and 20% for testing. The analysis results show that the linear regression model has a coefficient of determination (R²) of 0.77, which means that the model can explain 77% of the data variability. The prediction error value is low, with Mean Absolute Error (MAE) 0.06, Mean Squared Error (MSE) 0.01, and Root Mean Squared Error (RMSE) 0.08, indicating good accuracy. In the comparison between actual and predicted values, for actual data of 7,000 liters, the model predicts 7,984.70 liters. The variable number of customer transactions has the greatest influence on raw water demand, with a coefficient of 16,940.46, while seasonal factors have less influence. Based on these findings, it can be concluded that the linear regression algorithm is effective in predicting raw water demand, however further development is required to improve accuracy at extreme values, by adding variables or using more complex algorithms.
Co-Authors Agus Yuniawan Isyanto Arif Rinaldi Dikananda Arif Rinaldi Dikanda Candra, Candra Rahmawati Deny Indriyana Efendi Ica, Ica Pandia Kardina Martanto Masdarul Rizqi Muhamad Nurdin Yusuf Mulyawan . Reza Subagja Rohayati, Eti SULASTRI