Comparative Performance Analysis of Random Forest and Logistic Regression for Sentiment Classification of the Makan Bergizi Gratis Program on Platform X
DOI:
https://doi.org/10.63158/journalisi.v8i1.1371Keywords:
Sentiment Analysis, Random Forest, Logistic Regression, Makan Bergizi Gratis (MBG), Class ImbalanceAbstract
The rapid growth of e-commerce has made personalized product recommendations a crucial aspect of enhancing customer satisfaction and boosting sales. However, many small-to-medium-sized retail businesses, like Adiva Fashion Store, still rely on manual product selection through customer searches or seller recommendations, which often leads to challenges in meeting customer preferences. This study presents a case study of Adiva Fashion Store, where the Collaborative Filtering method was implemented to develop a personalized clothing product recommendation system. The item-based Collaborative Filtering approach was employed to calculate the similarity between products based on customer ratings and transaction history. These similarity values were then used to predict customer preferences for products that had not yet been purchased. The system was developed using the Waterfall methodology, which involved needs analysis, system design, implementation, testing, and maintenance. The results show that the recommendation system significantly improved the relevance of product suggestions, helping customers make better purchasing decisions and increasing sales effectiveness. This case study illustrates how data-driven recommendation systems can be effectively integrated into small-to-medium-sized retail environments, providing valuable insights for other businesses aiming to adopt similar strategies.
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References
[1] H. Rahman, M. Rahmah, dan N. Saribulan, “Efforts to Address Stunting in Indonesia: Bibliometric and Content Analysis (Upaya Penanganan Stunting Di Indonesia Analisis Bibliometrik dan Analisis Konten),” J. Ilmu Pemerint. Suara Khatulistiwa, vol. 8, no. 01, hal. 44–59, 2024, doi: 10.33701/jipsk.v8i1.3184.
[2] A. D. Arieffiani and M. R. L. Ekowanti, "Evaluating local government policy innovations: A case study of Surabaya's efforts in combating stunting and enhancing public health services quality," Jurnal Bina Praja, vol. 16, no. 1, pp. 1-20, 2024, doi: 10.21787/jbp.16.2024.1-20.
[3] A. Kiftiyah, F. A. Palestina, F. U. Abshar, dan K. Rofiah, “Makan Bergizi Gratis (MBG) Program in the Perspective of Social Justice and Socio – Political Dynamics,” Jurnal Keindonesiaan, vol. 05, no. 1, hal. 101–112, 2025, doi: 10.52738/pjk.v5i1.726.
[4] F. Fatkhurrohman, B. I. Nugroho, and N. Fadillah, " Sentiment Analysis of the Indonesian Government's Free Nutritional Meal Program Through Twitter Using the SVM Method (Analisis Sentimen Program Makan Bergizi Gratis Pemerintah RI Melalui Twitter Menggunakan Metode SVM)," RIGGS J. Artif. Intell. Digital Bus., vol. 4, no. 3, pp. 3906-3917, 2025, doi: 10.31004/riggs.v4i3.2533.
[5] E. Triningsih, M. Afdal, I. Permana, dan N. Evrilyan Rozanda, “Sentiment Analysis of the Free Nutritional Meal Program Using Machine Learning Algorithms on Social Media X (Analisis Sentimen Terhadap Program Makan Bergizi Gratis Menggunakan Algoritma Machine Learning Pada Sosial Media X),” Technol. Sci., vol. 6, no. 4, hal. 2240–2250, 2025, doi: 10.47065/bits.v6i4.6534.
[6] T. V. S. Krishna, T. S. R. Krishna, S. Kalime, C. V. M. Krishna, S. Neelima, dan R. R. Pbv, “A novel ensemble approach for Twitter sentiment classification with ML and LSTM algorithms for real-time tweets analysis,” Indones. J. Electr. Eng. Comput. Sci., vol. 34, no. 3, hal. 1904–1914, Jun 2024, doi: 10.11591/ijeecs.v34.i3.pp1904-1914.
[7] S. U. Hassan, J. Ahamed, dan K. Ahmad, “Analytics of machine learning-based algorithms for text classification,” Sustain. Oper. Comput., vol. 3, hal. 238–248, Jan 2022, doi: 10.1016/j.susoc.2022.03.001.
[8] A. Miftahusalam, A. Febby Nuraini, A. A. Khoirunisa, dan H. Pratiwi, “Comparison of Random Forest, Naïve Bayes, and Support Vector Machine Algorithms in Twitter Sentiment Analysis of Public Opinion on the Elimination of Honorary Staff (Perbandingan Algoritma Random Forest, Naïve Bayes, dan Support Vector Machine Pada Analisis Sentimen Twitter Mengenai Opini Masyarakat Terhadap Penghapusan Tenaga Honorer),” in Seminar Nasional Official Statistics, vol. 2022, no. 1, pp. 563-572, Nov. 2022, doi: 10.34123/semnasoffstat.v2022i1.1410.
[9] R. Jader dan S. Aminifar, “Predictive Model for Diagnosis of Gestational Diabetes in the Kurdistan Region by a Combination of Clustering and Classification Algorithms: An Ensemble Approach,” Appl. Comput. Intell. Soft Comput., vol. 2022, 2022, doi: 10.1155/2022/9749579.
[10] Intan Permata dan Esther Sorta Mauli Nababan, “Application of Game Theory in Determining Optimum Marketing Strategy In Marketplace,” J. Ris. Rumpun Mat. dan Iimu Pengetah. ALAM, vol. 2, no. 2, hal. 65–71, Jul 2023, doi: 10.55606/jurrimipa.v2i2.1336.
[11] V. Agresia dan R. R. Suryono, “Comparison of SVM, Naïve Bayes, and Logistic Regression Algorithms for Sentiment Analysis of Fraud and Bots in Purchasing Concert Ticket,” J. Inovtek Polbeng, vol. 10, no. 2, hal. 2025, 2025, doi: 10.35314/npyfdh47.
[12] T. R. Ramesh, U. K. Lilhore, M. Poongodi, S. Simaiya, A. Kaur, dan M. Hamdi, “Predictive Analysis Of Heart Diseases With Machine Learning Approaches,” Malaysian J. Comput. Sci., vol. 2022, no. Special Issue 1, hal. 132–148, 2022, doi: 10.22452/mjcs.sp2022no1.10.
[13] I. Hermansyah dan M. S. Hasibuan, “Sentiment Analysis of Free Nutritious Meal Programme on Social Media X using Linear Regression and Random Forest Algorithms,” PIKSEL Penelit. Ilmu Komput. Sist. Embed. Log., vol. 13, no. 1, hal. 55–68, 2025, doi: 10.33558/piksel.v13i1.10633.
[14] S. A. H. Bahtiar, C. K. Dewa, dan A. Luthfi, “Comparison of Naïve Bayes and Logistic Regression in Sentiment Analysis on Marketplace Reviews Using Rating-Based Labeling,” J. Inf. Syst. Informatics, vol. 5, no. 3, hal. 915–927, Agu 2023, doi: 10.51519/journalisi.v5i3.539.
[15] A. A. Rohman, A. G. Trisnapradika, dan K. Kunci, “Perbandingan Algoritma NBC, SVM, Logistic Regression untuk Analisis Sentimen Terhadap Wacana KaburAjaDulu di Media Sosial X,” Technol. Sci., vol. 7, no. 1, hal. 169–178, 2025, doi: 10.47065/bits.v7i1.7261.
[16] Tundo dan D. N. Rachmawati, “Implementasi Algoritma Naive Bayes untuk Analisis Sentimen,” J. Indones. Manaj. Inform. dan Komun., vol. 5, no. 3, 2024, doi: 10.35870/jimik.v5i3.978.
[17] M. Azhari dan P. Parjito, “Analisis Sentimen Opini Publik Program Makan Siang Gratis dengan Random Forest Pada Media,” Build. Informatics, Technol. Sci., vol. 6, no. 3, hal. 1932–1942, 2024, doi: 10.47065/bits.v6i3.6423.
[18] H. Junianto, R. E. Saputro, B. A. Kusuma, D. Intan, dan S. Saputra, “Comparison Of Logistic Regression And Random Forest In Sentiment Analysis Of Disdukcapil Application Reviews,” Jurnal Teknik Informatika (Jutif), vol. 5, no. 6, hal. 1539–1547, 2024, doi: 10.52436/1.jutif.2024.5.6.1802.
[19] M. Guia, R. R. Silva, and J. Bernardino, "Comparison of Naïve Bayes, Support Vector Machine, Decision Trees and Random Forest on Sentiment Analysis," KDIR, vol. 1, pp. 525-531, 2019.
[20] Z. S. Munmun, S. Akter, dan C. R. Parvez, “Machine Learning-Based Classification of Coronary Heart Disease: A Comparative Analysis of Logistic Regression, Random Forest, and Support Vector Machine Models,” OALib, vol. 12, no. 03, hal. 1–12, 2025, doi: 10.4236/oalib.1113054.
[21] T. Ahmed Khan, R. Sadiq, Z. Shahid, M. M. Alam, dan M. Mohd Su’ud, “Sentiment Analysis using Support Vector Machine and Random Forest,” J. Informatics Web Eng., vol. 3, no. 1, hal. 67–75, 2024, doi: 10.33093/jiwe.2024.3.1.5.
[22] M. E. Kabir, “Topic and sentiment analysis of responses to Muslim clerics’ misinformation correction about COVID-19 vaccine: Comparison of three machine learning models,” Online Media Glob. Commun., vol. 1, no. 3, hal. 497–523, 2022, doi: 10.1515/omgc-2022-0042.
[23] S. Das, K. Bhattacharyya, dan S. Sarkar, “Performance Analysis of Logistic Regression, Naive Bayes, KNN, Decision Tree, Random Forest and SVM on Hate Speech Detection from Twitter,” Int. Res. J. Innov. Eng. Technol., vol. 07, no. 03, hal. 07–03, 2023, doi: 10.47001/irjiet/2023.703004.
[24] S. Bhushan, M. Alshehri, I. Keshta, A. K. Chakraverti, J. Rajpurohit, dan A. Abugabah, “An Experimental Analysis of Various Machine Learning Algorithms for Hand Gesture Recognition,” Electron., vol. 11, no. 6, Mar 2022.
[25] M. G. Hussain, B. Sultana, M. Rahman, dan M. R. Hasan, “Comparison analysis of Bangla news articles classification using support vector machine and logistic regression,” Telkomnika (Telecommunication Comput. Electron. Control., vol. 21, no. 3, hal. 584–591, 2023, doi: 10.12928/TELKOMNIKA.v21i3.23416.
[26] M. Alfyando, Anggraeny Fetty Tri, dan SihanantoAndreas Nugroho, “Perbandingan Algoritma Random Forest dan Logistic Regression Untuk Analisis Sentimen Ulasan Aplikasi Tumbuh Kembang Anak Di Play Store,” J. Sist. Inf. dan Ilmu Komput., vol. 2, no. 1, hal. 77–86, 2023, doi: 10.59581/jusiik-widyakarya.v2i1.2262.
[27] G. A. Saputri dan D. Alita, “Analisis Sentimen Twitter Terhadap Pemindahan Ibu Kota Negara Menggunakan Support Vector Machine,” J. Inform. J. Pengemb. IT, vol. 9, no. 3, hal. 213–223, Des 2024, doi: 10.30591/jpit.v9i3.6612.
[28] R. Saputra dan F. N. Hasan, “Analisis Sentimen Terhadap Program Makan Siang & Susu Gratis Menggunakan Algoritma Naive Bayes,” J. Teknol. Dan Sist. Inf. Bisnis, vol. 6, no. 3, hal. 411–419, Jul 2024, doi: 10.47233/jteksis.v6i3.1378.
[29] Asro, Sudaryono, dan A. Sulaiman, “Analisis Sentimen tentang Transformasi Program Makan Siang menjadi Makan Bergizi Gratis menggunakan Logistik Regression pada laman Youtube,” J. ICT Inf. Commun. Technol., vol. 24, no. 1, hal. 87–94, 2024, doi: 10.36054/jict-ikmi.v24i1.257.
[30] A. Sitanggang, Y. Umaidah, Y. Umaidah, R. I. Adam, dan R. I. Adam, “Analisis Sentimen Masyarakat Terhadap Program Makan Siang Gratis Pada Media Sosial X Menggunakan Algoritma Naïve Bayes,” J. Inform. dan Tek. Elektro Terap., vol. 12, no. 3, 2024, doi: 10.23960/jitet.v12i3.4902.
[31] M. W. Arif, " Sentiment Analysis of the Free Nutritional Meal Policy on Social Media Using Python TextBlob-Based Natural Language Processing in Indonesia (Analisis Sentimen Kebijakan Makan Bergizi Gratis di Media Sosial Menggunakan Natural Language Processing Berbasis Python TextBlob di Indonesia)," J. Pendidik. dan Teknol. Indonesia (JPTI), vol. 5, no. 9, pp. 2463-2471, 2025., doi: 10.52436/1.jpti.931.
[32] Y. Al Amrani, M. Lazaar, dan K. E. El Kadirp, “Random forest and support vector machine based hybrid approach to sentiment analysis,” Procedia Comput. Sci., vol. 127, hal. 511–520, 2018, doi: 10.1016/j.procs.2018.01.150.
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