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All Journal Teknika Jurnal Sains dan Teknologi Jurnal Simetris JSI: Jurnal Sistem Informasi (E-Journal) International Journal of Advances in Intelligent Informatics IJCIT (Indonesian Journal on Computer and Information Technology) Jurnal Pilar Nusa Mandiri SINTECH (Science and Information Technology) Journal Jurnal Informatika Universitas Pamulang Jurnal Nasional Komputasi dan Teknologi Informasi WIDYA LAKSANA Jurnal Informatika Kaputama (JIK) EVOLUSI : Jurnal Sains dan Manajemen JTIK (Jurnal Teknik Informatika Kaputama) Jurnal Ilmu Teknik dan Komputer Jurnal Tekinkom (Teknik Informasi dan Komputer) Infotek : Jurnal Informatika dan Teknologi Jurnal Media Informatika JUSTIAN - Jurnal Sistem Informasi Akuntansi J-Intech (Journal of Information and Technology) Ilmu Komputer untuk Masyarakat Jurnal Pengabdian Masyarakat Bidang Sains dan Teknologi Jurnal Ilmu Komputer Dan Informatika Journal of Artificial Intelligence and Engineering Applications (JAIEA) Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI) Jurnal Abdimas Le Mujtamak Mestaka: Jurnal Pengabdian Kepada Masyarakat TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi Dedikasi Saintek Jurnal Pengabdian Masyarakat SOROT: Jurnal Pengabdian Kepada Masyarakat Madani: Jurnal Pengabdian Masyarakat dan Kewirausahaan Jurnal Komputer dan Teknologi (JUKOMTEK) DEDIKASI SAINTEK Jurnal Pengabdian Masyarakat Indonesian Community Service Journal of Computer Science (IndoComs) Jurnal Nasional Komputasi dan Teknologi Informasi
Riski Annisa
Universitas Bina Sarana Informatika
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Prediksi Produksi Kelapa Sawit Menggunakan Algoritma Machine Learning Berdasarkan Data Operasional 2025 Cici; Riski Annisa; Muhammad Fahmi Julianto
Jurnal Nasional Komputasi dan Teknologi Informasi Vol. 9 No. 3 (2026): Juni, 2026
Publisher : Program Studi Teknik Komputer, Fakultas Teknik. Universitas Serambi Mekkah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32672/h4zecc92

Abstract

Abstrak - Produksi kelapa sawit adalah salah satu parameter penting untuk melihat seberapa efisien operasi di perkebunan. Penelitian ini bertujuan untuk meramalkan produksi kelapa sawit (Janjang per Pokok) dengan menggunakan algoritma Machine Learning yang didasarkan pada data operasional tahun 2025. Dataset ini terdiri dari 1.347 catatan operasional yang mencakup 13 variabel fitur. Variabel-variabel ini termasuk luas lahan, produktivitas tenaga kerja, serta fitur turunan seperti Total_Hari kerja pemanen dan Hari Kerja per Hektar. Metode yang digunakan meliputi Regresi Linier, Regresi Pohon Keputusan, dan Regresi Hutan Acak dengan pembagian data sebesar 80% untuk latihan dan 20% untuk pengujian. Hasil evaluasi menunjukkan bahwa Random Forest Regressor memberikan kinerja terbaik dengan nilai R² mencapai 0,9393, Root Mean Square Error (RMSE) sebesar 0,0728, dan Mean Absolute Error (MAE) sebesar 0,0345. Kinerja ini jauh lebih baik dibandingkan dengan Decision Tree (R² 0,8372) dan Linear Regression (R² 0,7966). Analisis pentingnya fitur menunjukkan bahwa variabel Jumlah janjang/tandan dan Jumlah pohon sawit memberikan kontribusi paling besar untuk prediksi. kebaruan penelitian ini ada pada pengembangan fitur operasional khusus untuk perkebunan dan penggunaan pembelajaran ensemble pada data produksi nyata tahun 2025. Model ini diharapkan bisa menjadi alat yang membantu dalam pengambilan keputusan untuk mengoptimalkan penggunaan sumber daya saat panen. Kata kunci : Prediksi; Produksi Minyak Sawit; Pembelajaran Mesin; Hutan Acak; Data Operasional;   Abstract - Palm oil production is one of the important parameters to see how efficient the operation in the plantation. This study aims to forecast palm oil production (Fruits per Plant) using Machine Learning algorithms based on operational data in 2025. This dataset consists of 1,347 operational records that include 13 variable features. These variables include land area, labor productivity, as well as derived features such as Total_Harvester_Working_Days and Working_Days per Hectare. The methods used include Linear Regression, Decision Tree Regression, and Random Forest Regression with a data division of 80% for training and 20% for testing. The evaluation results show that Random Forest Regressor provides the best performance with an R² value reaching 0.9393, Root Mean Square Error (RMSE) of 0.0728, and Mean Absolute Error (MAE) of 0.0345. This performance is significantly better than Decision Tree (R² 0.8372) and Linear Regression (R² 0.7966). Feature importance analysis shows that the variables Number of bunches/stems and Number of oil palm trees provide the greatest contribution to the prediction. The novelty of this research lies in the development of operational features specifically for plantations and the use of ensemble learning on real production data for 2025. This model is expected to be a tool that helps in decision-making to optimize resource use during harvest. Keywords: Prediction; Palm Oil Production; Machine Learning; Random Forest; Operational Data;
Analisis Sentimen Ulasan Berbasis Lexicon Menggunakan Pembobotan Tf-IDF dan Algoritma Machine Learning Meltiana; Riski Annisa; Muhammad Fahmi Julianto
Jurnal Nasional Komputasi dan Teknologi Informasi Vol. 9 No. 3 (2026): Juni, 2026
Publisher : Program Studi Teknik Komputer, Fakultas Teknik. Universitas Serambi Mekkah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32672/gzkd8504

Abstract

Abstrak - Warung Kopi Asiang merupakan salah satu objek dalam penelitian ini, di mana ulasan pengguna yang terdapat di Google Maps dengan menggunakan kombinasi pendekatan berbasis Lexicon, pembobotan TF-IDF, dan algoritma machine learning. Sebanyak 2.358 ulasan dikumpulkan melalui teknik web scraping menggunakan Instant Data Scraper versi 1.4.1.Proses preprocessing diterapkan pada seluruh data,  mencakup tahapan case folding, cleaning, normalisasi, tokenization, stopword removal, dan stemming.Pelabelan sentimen dilakukan menggunakan metode berbasis lexicon, menghasilkan tiga kategori positif sebanyak 1. 289 ulasan (58,62%), negatif sebanyak 632 ulasan (28,74%), dan netral sebanyak 278 ulasan (12,64%). Data yang telah dilabeli kemudian dikonversi ke dalam bentuk numerik menggunakan TF-IDF dan diklasifikasikan dengan tiga algoritma yakni Support Vector Machine (SVM), Decision Tree, dan Logistic Regression. Optimasi  model dilakukan melalui hyperparameter tuning menggunakan GridSearchCV. Dari hasil evaluasi  Support Vector Machine (SVM) dan Logistic Regression sama-sama mencapai akurasi tertinggi sebesar 80%, di mana pada SVM akurasi optimal tersebut sudah tercapai sejak model dasar (default).Decision Tree menghasilkan akurasi sebesar 71%. Temuan ini membuktikan bahwa integrasi metode Lexicon, TF-IDF, dan machine learning bekerja secara efektif dalam menganalisis sentimen ulasan di Google Maps. Kata kunci : Analisis sentimen; Google Maps; Lexicon; Machine Learning;   Abstract - Warung Kopi Asiang is one of the subjects of this study, in which user reviews found on Google Maps were analysed using a combination of a lexicon-based approach, TF-IDF weighting, and machine learning algorithms. A total of 2,358 reviews were collected via web scraping using Instant Data Scraper version 1.4.1. Preprocessing was applied to all data, covering the stages of case folding, cleaning, normalisation, tokenisation, stopword removal, and stemming; sentiment labelling was performed using a lexicon-based method, resulting in three categories: 1, 289 reviews (58.62%), 632 negative reviews (28.74%), and 278 neutral reviews (12.64%). The labelled data was then converted into numerical form using TF-IDF and classified using three algorithms: Support Vector Machine (SVM), Decision Tree, and Logistic Regression. Model optimisation was carried out via hyperparameter tuning using GridSearchCV. The evaluation results showed that both Support Vector Machine (SVM) and Logistic Regression achieved the highest accuracy of 80%, with SVM having reached this optimal accuracy from the base (default) model. Decision Tree achieved an accuracy of 71%. These findings demonstrate that the integration of the Lexicon method, TF-IDF, and machine learning works effectively in analysing sentiment in Google Maps reviews Keywords: Sentiment analysis; Google Maps; Lexicon; Machine Learning;
Perbandingan Algoritma Machine Learning Berbasis TF-IDF dan SMOTE untuk Analisis Sentimen Ulasan Aming Coffee Emi Wulandari; Riski Annisa; Muhammad Fahmi Julianto
Jurnal Nasional Komputasi dan Teknologi Informasi Vol. 9 No. 3 (2026): Juni, 2026
Publisher : Program Studi Teknik Komputer, Fakultas Teknik. Universitas Serambi Mekkah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32672/hk03ka40

Abstract

Abstrak - Ulasan pelanggan pada Google Maps dapat dimanfaatkan untuk mengetahui persepsi pelanggan terhadap kualitas produk dan layanan suatu perusahaan. Namun, banyaknya jumlah ulasan dan data yang tidak terstruktur menyebabkan proses analisis secara manual menjadi kurang efektif. Penelitian ini bertujuan untuk melakukan analisis sentimen pada ulasan pelanggan Aming Coffee menggunakan metode pembobotan Term Frequency–Inverse Document Frequency (TF-IDF) dan algoritma machine learning. Data penelitian diperoleh melalui proses scraping sebanyak 4.000 ulasan pelanggan dari Google Maps Aming Coffee. Tahapan penelitian meliputi preprocessing data yang terdiri atas cleaning, case folding, tokenisasi, normalisasi, stopword removal, dan stemming, kemudian dilakukan pembobotan TF-IDF, pembagian data, klasifikasi menggunakan algoritma Naïve Bayes, Support Vector Machine (SVM), dan Decision Tree C4.5, serta penerapan Synthetic Minority Over-sampling Technique (SMOTE) pada data latih untuk menangani ketidakseimbangan kelas. Evaluasi model dilakukan menggunakan accuracy, precision, recall, F1-score, dan confusion matrix. Hasil penelitian menunjukkan bahwa model tanpa SMOTE memperoleh akurasi tertinggi, yaitu Naïve Bayes sebesar 90,63%, diikuti SVM sebesar 90,50%, dan C4.5 sebesar 88,25%. Setelah penerapan SMOTE, akurasi model mengalami penurunan, namun nilai precision meningkat sehingga model menjadi lebih mampu memperhatikan kelas sentimen minoritas. Hasil Exploratory Data Analysis (EDA) menunjukkan bahwa sentimen positif mendominasi ulasan pelanggan Aming Coffee dengan kata yang paling sering muncul antara lain “kopi”, “coffee”, “enak”, “mantap”, dan “ramai”. Penelitian ini menunjukkan bahwa kombinasi TF-IDF, machine learning, dan SMOTE dapat digunakan untuk analisis sentimen ulasan pelanggan serta memberikan informasi yang bermanfaat dalam evaluasi kualitas layanan dan pengambilan keputusan bisnis. Kata kunci : Analisis Sentimen; TF-IDF; Naïve Bayes; Support Vector Machine; Decision Tree C4.5; SMOTE;   Abstract - Customer reviews on Google Maps can be utilized to understand customer perceptions of a company's products and services. However, the large volume of unstructured reviews makes manual analysis less effective. This study aims to perform sentiment analysis on Aming Coffee customer reviews using the Term Frequency–Inverse Document Frequency (TF-IDF) weighting method and machine learning algorithms. Research data were collected through scraping 4,000 customer reviews from Google Maps. The research stages included data preprocessing consisting of cleaning, case folding, tokenization, normalization, stopword removal, and stemming, followed by TF-IDF weighting, data splitting, sentiment classification using Naïve Bayes, Support Vector Machine (SVM), and Decision Tree C4.5 algorithms, as well as the implementation of Synthetic Minority Over-sampling Technique (SMOTE) on training data to address class imbalance. Model evaluation was conducted using accuracy, precision, recall, F1-score, and confusion matrix. The results showed that models without SMOTE achieved the highest accuracy, with Naïve Bayes reaching 90.63%, followed by SVM at 90.50% and C4.5 at 88.25%. After applying SMOTE, model accuracy decreased, while precision increased, indicating improved attention to minority sentiment classes. Exploratory Data Analysis (EDA) results revealed that positive sentiment dominated Aming Coffee customer reviews, with frequently occurring words including “kopi,” “coffee,” “enak,” “mantap,” and “ramai.” This study demonstrates that the combination of TF-IDF, machine learning algorithms, and SMOTE can be effectively applied to sentiment analysis and provide useful insights for service quality evaluation and business decision-making. Keywords: Sentiment Analysis; TF-IDF; Naïve Bayes; Support Vector Machine; Decision Tree C4.5; SMOTE;
Co-Authors Agnes, Veronika Agung Sasongko Alvin Zikirlah, Hikmawan Anna Anna Anna Anna Anna Anna Anna Anna, Anna Anna, Anna - Arvin Hidayat Waluya Baihaqi, Muhammad Rivaldi Chania Lista Zepani Chika Suhendra Cici Debi Handika Detylia Detylia Dewi Ayu Nur Wulandari Didi Rosiyadi Dwiza Riana Eliska, Puspa Emi Wulandari Eva Meilinda Fadilah, Aldi Famela Jessica Fariska Adela Nurhidayah Fazilla, Muhammad Firly Annisa Luthfi Fransiska Regina Frizi Olivian Grant Christo Budiyanto Hazael Susanto Hidayana, Reymond Syahputra Iltavera Paula Lady Agustin Fitriana Lestari Yusuf Marelita Marelita Maulana, Dinar Ridho Maulana, Reza - Meltiana Mohammad Kamal Reza Monikka Nur Winnarto Muhamad Rizki Pratama Muhammad Eka Fadillah Muhammad Fahmi Julianto Muhammad Fazilla Murdiansyah Murdiansyah Mutia Sari, Elsa Nur W, Monikka Nurfia Oktaviani Syamsiah Panny Agustia Rahayuningsih Parwandar, Robi Paula, Iltavera Rachman, Muhammad Arief Rahayuningsih, Agustia Rai Markus Panamuan Raja Sabaruddin Raja Sabaruddin Raja Sabarudin Ramadhani, Zulfikar Ismaya Rendi Rendi Reymond Syahputra Hidayana Robi Parwandar Sahrul Bariyah Sendi Yustisio Siti Nurdiani Sri Murni Suhendra, Chika Talcha Ilham Putri Timothy, Billianto Wahyu Nugraha Weiskhy Steven Dharmawan Welly, Welly Winnaarto, Monikka Nur Winny Christiani Thomas Yulia Yulia Yustisio, Sendi Zikirlah, Hikmawan Alvin