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All Journal JSI: Jurnal Sistem Informasi (E-Journal) International Conference on Law, Business and Governance (ICon-LBG) FIAT JUSTISIA: Jurnal Ilmu Hukum Jurnal Cita Hukum Seminar Nasional Informatika (SEMNASIF) ICON-CSE MODELING: Jurnal Program Studi PGMI Jurnal Sains dan Informatika JURTEKSI alfazuna: Jurnal Pembelajaran Bahasa Arab dan Kebahasaaraban Integrated Lab Journal JATI (Jurnal Mahasiswa Teknik Informatika) Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) Jurnal Pendidikan dan Teknologi Indonesia Khatulistiwa: Jurnal Ilmu Pendidikan Administrative and Environmental Law Review Muhammadiyah Law Review Journal of Ocean, Mechanical and Aerospace -science and engineering- (JOMAse) Al-MIKRAJ: Jurnal studi Islam dan Humaniora Indonesian Journal Computer Science (ijcs) Malcom: Indonesian Journal of Machine Learning and Computer Science Jurnal Pengabdian West Science ABDIMASY: Jurnal Pengabdian Kepada Masyarakat Jurnal ilmiah teknologi informasi Asia Jurnal PETISI (Pendidikan Teknologi Informasi) JuTISI (Jurnal Teknik Informatika dan Sistem Informasi)
Fathoni Fathoni
Fakultas Hukum Unversitas Lampung
Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Mechanical Engineering Public Health Social Sciences Other

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Prediksi Lead Scoring untuk Optimasi Penjualan Menggunakan Random Forest dan Teknik SMOTE Pratama Putra, Daffa; Agil Kusuma, Dimas; Al Akbar, M. Rizki; Ibrahim, Ali; Fathoni, Fathoni
JURNAL FASILKOM Vol. 16 No. 1 (2026): Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer)
Publisher : Unversitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/jf.v16i1.11292

Abstract

Accurate lead scoring systems have become a strategic necessity for organizations operating in data-driven marketing environments, as they enable systematic identification of high-value customer prospects to maximize sales conversion efficiency. A fundamental challenge confronting conventional classification models is the class imbalance inherent in real-world marketing data, which induces majority-class bias and substantially reduces sensitivity toward minority-class prospects. This study proposes a Random Forest (RF)-based lead scoring prediction model integrated with the Synthetic Minority Over-sampling Technique (SMOTE) to address this limitation systematically. The dataset employed is the Lead Scoring Dataset from Kaggle, comprising 9,240 customer prospect records from an educational company with a class imbalance ratio of 1.59:1. Preprocessing included missing value treatment, removal of attributes exceeding 40% data loss, mode-based imputation, and categorical feature encoding. Following an 80:20 stratified split, SMOTE was applied exclusively to the training set to produce a balanced class distribution and prevent data leakage. The RF model was configured with n_estimators = 100, max_features = 'sqrt', and class_weight = 'balanced'. The proposed RF+SMOTE model achieved accuracy of 88.80%, precision of 86.44%, recall of 84.13%, F1-Score of 85.27%, and AUC-ROC of 0.9453, outperforming the baseline across four of five evaluation metrics. The most notable improvement was observed in recall, with a gain of 1.26 percentage points. Stratified 5-Fold Cross-Validation confirmed robust generalization capability, with AUC-ROC values consistently ranging between 94% and 95%. These findings demonstrate that the hybrid RF+SMOTE approach effectively enhances high-potential prospect detection while maintaining overall model stability for real-world Customer Relationship Management (CRM) deployment.
Klasifikasi Opini Tidak Informatif Pada Program Makan Bergizi Gratis (MBG) Menggunakan Random Forest Syabilla, Lailla Syal; Natasyah, Mei Intan; Fathoni, Fathoni; Siahaan, Jeremiah Alwin
Indonesian Journal Computer Science Vol. 5 No. 1 (2026): April 2026
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/ijcs.v5i1.12509

Abstract

Pemerintahan Prabowo-Gibran meluncurkan kebijakan strategis Program Makan Bergizi Gratis (MBG) untuk menjamin pemenuhan hak dasar anak atas pangan yang aman, sehat, dan bergizi. Urgensi program ini didasarkan pada angka stunting di Indonesia tahun 2024 yang mencapai 14%, sehingga peluncuran kebijakan Program Makan Bergizi Gratis (MBG) memicu diskusi publik yang masif di platform media sosial X dengan jumlah pengguna mencapai 24,7 juta orang. Namun, volume data yang besar tersebut menghadirkan masalah "Data Sampah" (Noise) berupa spam, promosi jualan, hingga akun bot yang berpotensi menyebabkan bias pada analisis opini publik terhadap program Program Makan Bergizi Gratis (MBG). Penelitian ini bertujuan membangun model klasifikasi opini tidak informatif dengan mengimplementasikan algoritma Random Forest berbasis Knowledge Discovery in Database (KDD) sebagai tahap pra-pemrosesan sebelum analisis sentimen lanjutan. Data yang digunakan berjumlah 10.000 tweet bersumber dari Kaggle, diproses melalui lima tahapan KDD meliputi Data Selection, Data Preprocessing, Data Transformation, Data Mining, dan Data Evaluation dengan menggunakan RapidMiner. Representasi fitur dilakukan dengan pembobotan TF-IDF dan validasi model menggunakan k-fold Cross Validation dengan k=10. Hasil evaluasi menunjukkan model mencapai akurasi 82,03%, precision 93,78%, recall 68,61%, dan F-Measure 79,24%. Hasil ini membuktikan bahwa pendekatan KDD berbasis Random Forest efektif digunakan sebagai pipeline filter noise yang terstruktur untuk teks media sosial berbahasa Indonesia, khususnya pada domain opini kebijakan pemerintah.
Multi-Source Sentiment Analysis of Shopee Tokopedia Using Hybrid Machine Learning for Customer Relationship Management Optimization Kurniasari, R. Nyi Pipih; Ramadhani, Muthia; Amanda, Khansa Putri; Fathoni, Fathoni; Ibrahim, Ali
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 6 No. 3 (2026): MALCOM July 2026
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v6i3.2672

Abstract

Sentiment analysis on marketplace customer reviews is important for understanding user perceptions and supporting Customer Relationship Management (CRM) strategies. This study proposes a multi-source sentiment analysis approach based on big data from Shopee and Tokopedia platforms using Hybrid Machine Learning. The research process includes data collection, preprocessing, TF-IDF feature extraction, and classification using Support Vector Machine (SVM) and Random Forest techniques. The preprocessing stage consists of case folding, tokenization, stopword removal, and stemming to improve the quality of textual data. The TF-IDF method is used to transform text data into numerical features before classification. The evaluation results show that the SVM model achieved an accuracy of 97.49%, while the Random Forest model achieved 97.47%. The sentiment distribution indicates a strong positive bias, reflecting high customer satisfaction with marketplace services. However, negative sentiment persisted, mainly due to delivery delays, application errors, and customer service issues. The proposed hybrid approach can provide data-driven insights to improve service quality and support decision-making in CRM strategies.
Co-Authors Abdul Aziz Zaenal Buchori Adeliani, Adeliani Aditya Ainul Haqiqi Agil Kusuma, Dimas Agus Pracoyo Akbar Kurniawan, Iqbal Akrom, Muhammad Adib Al Akbar, M. Rizki Al Mas Ud, Khalid Aldhy Rizhaldy S.G Alghifari, Muhammad Ali Ibrahim Alifayoezra, Muhammad Dzaky Amanda, Khansa Putri Amelia Amelia Andriani Parastiwi Angelina Tompunu, Keisha Anggina, Edith Apriansyah Putra Arba'i, Sultan Ari Wedhasmara Asyiq, Abdulloh Athallah, Deni Auliya, Lana Nur Azmi Zaky, Muhammad Baidhawi, Alif Dedy Kurniawan Demetria, Putri Dewi Aprilliana Aprilliana Donny Radianto Dwiyansyah, Octa Egga Asoka Eka Afrianti Fachry Abda El Rahman Faiq, Al Ikhsan Fauzi, Muhamad Rizal Firman Muntaqo Gurruh Dwi Septano Hadipurnawan Satria Hariza Marshella, Siti Hendrawan, Deni Agus Heni Siswanto Herman Hariyadi Ari Murtono Hieronymus Soerjatisnanta Huda, Hisbullah Ikbal Ikbal Inda Kesuma S Istiqomah, Amalia Windy James Reinaldo Jodi Pratama, Muhammad Komarudin Achmad Kurniasari, R. Nyi Pipih Luh Putu Ratna Sundari M Baihaqi M. Alfanshuril Hakim Maharani Maharani, Maharani Maroni Maroni Masrury, Farhan Mohammad Khalid Mohammad Luqman Muarif, Moh. Syamsul Muhammad Adryan Munir Rifa'i Muhammad Akib Muhammad Dzulkifli Muhammad Kurniawan, Hafiz Muhammad Naufal Suhaimi Nabilatulrahmah, Raihana Natasyah, Mei Intan Nicky Andre Prabatama NIZAR, MOHAMMAD Nugraha, Allan Patma, Tundung Subali Pratama Putra, Daffa Putri, Salsanabila Mariestiara Putri, Septhia Charenda Rachmad, Muhammad Ichsan Farrel Rahmat Izwan Heroza Raihana Putri, Naila Ramadhani, Muthia Riansyah, M Bintang Naufal Risyahputri, Aliyananda Rizka Mumtaz, Fadia Rizki, Raditya Dafa Rulyanti Dyah Prawesti Saimi, Saimi Salsa Kinanty, Reina Siahaan, Jeremiah Alwin Sidik Nurcahyo Siswoko Siswoko Sofuan Jauhari Sony Oktapriandi Sriwijaya, Sayid Bahri Suci Fitriani, Suci Syabilla, Lailla Syal Syahputra Zaki, Imam Syahrul Akhmal Hidayatulloh Tammam, Bimmo Fathin Tarmukan Tarmukan Therina Lakeisyah, Eka Tri Alfandy, Muhammad Wahyu Tri Wahono Widia Wahyuningtyas F Winarno, Totok Yanis Alhafidz Akhmad Yudith Mimbar Ali Sakti Yulianto Yulianto Zahrona Arifatul Maula