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All Journal International Journal of Public Health Science (IJPHS) ComEngApp : Computer Engineering and Applications Journal Al-'Adalah Jurnal Psikologi Teori dan Terapan Jurnal CoreIT INOVTEK Polbeng - Seri Informatika Jurnal Pengabdian Masyarakat MIPA dan Pendidikan MIPA Jurnal Informatika Universitas Pamulang Building of Informatics, Technology and Science Piwulang : Jurnal Pendidikan Bahasa Jawa Information System Journal (INFOS) Bulletin of Information Technology (BIT) Sevanam: Jurnal Pengabdian Masyarakat Malcom: Indonesian Journal of Machine Learning and Computer Science INJURITY: Journal of Interdisciplinary Studies Jurnal Kefarmasian Indonesia
Lestari Handayani
Pusat Humaniora, Kebijakan Kesehatan dan Pemberdayaan Masyarakat Jl Indrapura No. 17 Surabaya
Religion Arts Humanities Chemistry Computer Science & IT Economics, Econometrics & Finance Education Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Medicine & Pharmacology Public Health Social Sciences

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APPLICATION OF K-NEAREST NEIGHBOR REGRESSION METHOD FOR RICE YIELD PREDICTION Handayani, Lestari; Alfarabi.B, Alif; Aprilia, Tasya; Wulandari, Indah; Jasril, Jasril; Ramadhani, Siti; Budianita, Elvia
Jurnal CoreIT: Jurnal Hasil Penelitian Ilmu Komputer dan Teknologi Informasi Vol 11, No 1 (2025): June 2025
Publisher : Fakultas Sains dan Teknologi, Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/coreit.v11i1.30907

Abstract

Rice plants with the Latin name Oryza Sativa are food plants that are widely used as the main food crop in various countries, one of which is Indonesia. Indonesia is ranked 4th as the largest rice consuming country in the world. This requires the availability of rice to be maintained. Unstable rice production can be a problem. One of the districts that has experienced a decline in rice production in recent years is the district of Lima puluh kota located in West Sumatra province. This requires prediction of rice production so that it can be used as a benchmark for the future. This study uses data on rice production in fifty cities from 2013 to 2023. The method used to predict is k-nearest neighbor regression (KNN Regression). The data division uses rasio 90 : 10. In testing the data used is divided into 2, namely normal data and data that has been normalized. The test results produce the smallest mean absolute percentage error (MAPE) value of 6.98% on normal data, the value of k is 6 with data division using k-fold 5. Based on the resulting MAPE value, it can be said that KNN Regression can predict rice production results very accurately.
Klasifikasi Sentimen Tweet Masyarakat terhadap Kendaraan Listrik Menggunakan Support Vector Machine Ananda, Nuari; Fikry, Muhammad; Yusra, Yusra; Handayani, Lestari; Iskandar, Iwan
Jurnal Informatika Universitas Pamulang Vol 8 No 4 (2023): JURNAL INFORMATIKA UNIVERSITAS PAMULANG
Publisher : Teknik Informatika Universitas Pamulang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32493/informatika.v8i4.36754

Abstract

Sentiment analysis involves using classification algorithms to analyze public opinions and feelings in text. Within the automobile industry, electric vehicles (EVs) stem from the circular economy and represent a novel technology under investigation in sentiment classification studies. The Support Vector Machine (SVM) algorithm is commonly used in this research due to its superior accuracy compared to other algorithms. The goal of this study is to apply SVM variable selection techniques to enhance sentiment analysis quality. Python is the programming language used to build the sentiment classification model, which involves feature selection using TF-IDF, training with cross-validation and grid search, evaluation using a confusion matrix, and storing the dataset in a MySQL database. The research focuses on the sentiment classification of 3000 public tweets about electric vehicles on Twitter. Through various scenarios, it was observed that the accuracy of sentiment classification varied depending on factors such as randomizing data, handling negation, and using different types of features like unigrams or bigrams. The highest accuracy achieved was 84% using a scenario with random data, negation handling, and unigram features. Overall, this research highlights the impact of randomizing data and selecting appropriate features on sentiment classification accuracy for electric vehicles on Twitter.
Co-Authors Abdurrahman Asysyafaat, Alfian Adha, Martin Agustian, Surya Ahmad Fauzan Al Fiqri, M. Faiz Alfarabi.B, Alif Ali Abdul Wakhid Ananda, Nuari Aprilia, Tasya Arasy, Abdurrahman Ardhia, Dhiana Nur Aziz, Khafidh Nur Betty Roosihermiatie Deden Deden, Deden DESI NURWIDAWATI Dewi Diniaty Diah Kurnia Mirawati Diba, Berliana Farah Dodi Budirokhman Eka Pandu Cynthia Elvia Budianita Febi Yanto, Febi Febrianto, Soka Fitri Insani Hafizhan, Muhammad Hambali, Basyir Heriza, Meilinda Iffatin Nur Indah Wulandari Ismu Kusumanto Iwan Iskandar Jannata, Nanda Jasril Jasril Jasril Jasril Kurnia Rahman, Fikri Kurniasari, Yuni Lestari, Aprili Cahya Lucie Widowati Lulut Kusumawati Maharani, Yossiana Diva Megawati Muhammad Fikri Muhammad Fikry Muhammad, Novan Aulia Mujimin - Nugraheni, Eva Dwi Pizaini Pizaini, Pizaini Ramadhani, Siti Sri Anjani Stefanus Erdana Putra Subandi Subandi Sucipto Hadi Purnomo Suharmiati Suharmiati Suwanto Sanjaya Tri Juni Angkasawati Tsary, Keysa Inas Wulandari, Oktaviana Yusra, Yusra