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Fathoni -
Information Systems Universitas Sriwijaya Palembang, Indonesia
Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Neuroscience Public Health Other

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Analisis Sentiment Pelanggan Terhadap Penilaian Produk Pada Toko Online Shop Amreta Menggunakan Metode Naïve Bayes Classification Alisia Silver Stone; Fathoni Fathoni
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 6, No 3 (2022): Juli 2022
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v6i3.4436

Abstract

Sentiment analysis or opinion mining is an analysis that aims to see the sentiment of people or groups regarding certain entities. The sentiments expressed by society can be in positive, negative and neutral form. One media that can be given an opinion by the public is in the e-commerce application,  namely the shopee application, shopee has a comment or assessment feature on the product that has been purchased. Toko which was used as a sample of researchersis an amreta online shop store  , based on the results of the identification of the problem, it was found that the fact was that many comments did not match the stars given so it can be said that the rating cannot represent that the store's performance is good or not. Therefore, to increase the profit of shop work, the amreta still needs to evaluate the store. In conducting an evaluation, the store needs to classify positive, negative or neutral comments. Analysis of customer sentiment towards product assessments in amreta online shop stores using the naive bayes classification method. The use of test data in this study was obtained from the sentiment of amreta online shop consumers as much as 2014 data,then the data was processed through  the data cleaning process  resulting in net data of 1899 data. Furthermore, the data preprocessing process is divided into 3 stages, namely Tokenize Data, Transform case and Stopword removal. After that, the analysis of data for the automatic labeling stage using Text Vectorize from the process obtained data division into 3 data groups of 71% or 1343 positive data, 3% or 52 negative data and 26% or 504 neutral data.  furthermore, it is processed using rapidminer tools while for operators in the form of algorithms using the Sentiment Naïve Bayes Classification model  through automatic calculations.  The results of the study can be concluded that the test data obtained have an accuracy level of 97.16% using the Naive Bayes Classification model.
Sentiment Analysis Pada Masyarakat Terhadap LRT Kota Palembang Menggunakan Metode Improved K-Nearest Neighbor Siti Nur Arafah; Fathoni Fathoni
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 6, No 3 (2022): Juli 2022
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v6i3.4434

Abstract

The LRT is a sustainable fast transportation system, which was built to overcome the congestion problem in the city of Palembang. In order to attract people's interest to switch to using public transportation compared to private transportation, one of them is by improving the quality of services provided. Sentiment analysis is used to classify positive and negative opinions on users of Palembang City LRT transportation services. In addition to retrieving data through crawling data on tweet data, the researchers also distributed questionnaires. In conducting the classification process of sentiment analysis, this study uses the Improved K-Nearest Neighbor method which is a modification of the K-Nearest Neighbor method. The results of this research are testing and training data on 1617 data records and the highest accuracy of 74.07% on 90% training data and 10% testing data, with 70% precision, 56% recall and 59% f-1 score, while the lowest accuracy with an accuracy of 63.04% on 50% training data and 50% testing data, with 44% precision, 42% recall and 42% f-1 score
Implementasi E-learning sebagai Komplemen dan Blanded Learning Untuk Meningkatkan Motivasi dan Hasil Belajar Pada Matakuliah Enterprise Resources Planning Fathoni -
Jurnal Sistem Informasi Vol 7, No 1 (2015)
Publisher : Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (674.909 KB) | DOI: 10.36706/jsi.v7i1.1975

Abstract

Abstract   One of the main problems of education system in Indonesia is quality and output of learning process. This problem relates with the teaching and learning material availability which access is still constrained by time and distance. To overcome this problem, it needs a change in teaching and learning process paradigm as applying e-learning so that lifetime education for everyone can be implemented. The strategy of using e-learning for ERP subject as a part of learning can improve motivation and output of the teaching and learning process. To reach this goal, an e-learning model is developed. This model is supported by qualified multimedia teaching material which interest students to have blended learning, on-line interaction and discussions as well as complementing teaching and learning material through e-learning media. Keywords : e-learning, complement and blended learning, Motivation dan learning output. Abstrak   Salah satu masalah utama pada sistem pendidikan di Indonesia adalah masalah kualitas dan hasil dari proses pembelajaran. Masalah ini berhubungan dengan penyediaan materi dan bahan belajar yang belum dapat diakses secara luas tanpa dibatasi oleh kendala jarak dan waktu. Apabila kendala ini dapat diatasi maka misi untuk menerapkan pendidikan sepanjang hayat pada segenap lapisan masyarakat dapat diwujudkan. Dalam mewujudkan hal ini dibutuhkan perubahan pada paradigma proses belajar mengajar yang telah diterapkan selama ini seperti mengimplementasikan e-learning. Strategi penggunaan e-learning pada matakuliah ERP sebagai bagian dari proses pembelajaran dapat meningkatkan motivasi dan hasil pembelajaran yang telah dilakukan. Untuk mencapai tujuan tersebut dikembangkan model pembelajaran e-learning yang tepat  dan didukung bahan ajar multimedia yang berkualitas dan dapat menarik minat mahasiswa sehingga mahasiswa termotivasi untuk aktif belajar mandiri (blanded), berdiskusi dan berinteraksi secara on-line serta saling memperkaya materi ajar (komplemen) learning melalui media e-learning. Kata kunci : e-learning, komplemen dan blanded learning, Motivasi dan Hasil Belajar.
Multilabel sentiment analysis for classification of the spread of COVID-19 in Indonesia using machine learning Fathoni Fathoni; Erwin Erwin; Abdiansah Abdiansah
Indonesian Journal of Electrical Engineering and Computer Science Vol 31, No 2: August 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v31.i2.pp968-978

Abstract

This study aims to use datasets on Twitter to find out public opinion on the spread of coronavirus in Indonesia by conducting sentiment analysis. The resulting sentiment analysis will benefit the community by helping the Indonesian government take various strategic measures to prevent and counter the spread of the COVID-19. This research was conducted through the data collection stage, namely crawling data tweet words in Bahasa Indonesia containing the meaning of the spread of COVID-19, the next stage of the process of creating labels manually. Next, the pre-process stage by removing the character, symbols and special features from Twitter. The last stage, classification using learning machine with 3(three) methods namely K-nearest neighbor (K-NN), Naïve Bayes and decision tree. The study analyzed sentiment of 1,119 valid Tweets data and found that K-NN algorithm had the highest accuracy value compared to Naïve Bayes and decision tree algorithms, which was 95.10%. However, the Twitter data analyzed obtained 78.19% of Tweets that fall into the negative category and only 13.85% of public opinion that is positive. This indicates that most of the Tweets of Indonesians in twitter do not mean the spread of COVID-19 disease somewhere.
PELATIHAN PENGEMBANGAN MEDIA PEMBELAJARAN BERBASIS ICT GURU SMP/MTs YANG TERGABUNG MUSYAWARAH GURU MATA PELAJARAN (MGMP) KOTA PANGKALPINANG Ibrahim, Ali; Meiriza, Allsela; -, Fathoni; Utama, Yadi; Sanjaya, M. Rudi; Rezqe, Beriadi Agung Nur; Alzaini, Akbar
Jurnal Pengabdian Masyarakat Bumi Rafflesia Vol. 3 No. 1 (2020): Jurnal Pengabdian Masyarakat Bumi Raflesia
Publisher : Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Guru mata pelajaran dituntut untuk memiliki kemampuan professional yang sesuai dengan Standar Nasional Pendidikan, yaitu setiap guru harus memiliki kompetensi pedagogic, kepribadian, professional dan social. Pengembangan keempat kompetensi tersebut perlu terus dikembangkan, baik melalui pendidikan, pelatihan, maupun kerja kelompok atau organisasi profesi guru. Rendahnya hasil belajar siswa salah satunya disebabkan karena guru belum menggunakan media animasi dalam proses pembelajaran. Proses pembelajaran masih berlangsung secara konvensional, dimana aktivitas menulis lebih dominan dilakukan oleh guru dalam mengajar. Dengan pelatihan program Quipper School dapat meningkatkan keterampilan para guru dalam membuat media ajar berbasis multimedia. program Quipper School adalah platform sekolah digital tanpa biaya. Melalui platform ini, guru dapat mengirim dan mengelola materi pembelajaran, ujian, serta nilai siswa. Metode yang digunakan dalam pelatihan adalah dengan cara peragaran dan demontrasi program Quipper School. Dari hasil kuisiner setelah pelatihan 90 % peserta berhasil dalam membuat media ajar.
Pelatihan Pemanfaatan Teknologi Informasi dan Digital Marketing untuk Meningkatkan Daya Saing UMKM di Desa Sungai Rebo Ali Ibrahim; Endang Lestari Ruskan; Ermatita Ermatita; Fathoni Fathoni; Rizka Dhini Kurnia; Al farissi; Ahmad fali Oklilas; Yadi Utama; Purwita Sari; Naretha Kawadha Pasemah Gumay
Mejuajua: Jurnal Pengabdian pada Masyarakat Vol. 5 No. 3 (2026): April 2026
Publisher : Yayasan Penelitian dan Inovasi Sumatera (YPIS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52622/mejuajuajabdimas.v5i3.339

Abstract

UMKM memiliki peran strategis dalam mendorong pertumbuhan ekonomi lokal, termasuk di Desa Sungai Rebo, Kecamatan Banyuasin I, yang sebagian besar masyarakatnya menggantungkan penghasilan dari usaha kecil di sektor kuliner, kerajinan, dan perdagangan rumah tangga. Meskipun demikian, rendahnya pemanfaatan teknologi informasi serta keterbatasan kemampuan dalam pemasaran digital menyebabkan daya saing produk UMKM di desa tersebut belum berkembang secara optimal. Kondisi ini membuat jangkauan pemasaran produk masih terbatas pada pasar lokal dan belum mampu memanfaatkan peluang pasar yang lebih luas melalui media digital. Kegiatan pengabdian kepada masyarakat ini bertujuan untuk meningkatkan daya saing UMKM di Desa Sungai Rebo melalui penerapan teknologi informasi dan digital marketing sebagai strategi promosi produk secara berkelanjutan. Program ini dilaksanakan melalui pendekatan pelatihan dan pendampingan yang melibatkan pelaku UMKM sebagai mitra utama dalam setiap tahapan kegiatan, mulai dari identifikasi kebutuhan, pelatihan, hingga evaluasi hasil. Metode kegiatan meliputi asesmen awal untuk memetakan kemampuan digital pelaku UMKM, pelatihan penggunaan teknologi informasi dasar seperti pengelolaan perangkat digital dan aplikasi pengolahan foto produk, serta pelatihan digital marketing yang mencakup pembuatan konten promosi dan pemanfaatan media sosial. Selain itu, dilakukan pendampingan intensif dalam implementasi strategi digital marketing sesuai dengan karakteristik usaha masing-masing peserta. Hasil kegiatan menunjukkan adanya peningkatan kemampuan pelaku UMKM dalam memanfaatkan teknologi informasi untuk mendukung kegiatan usaha dan memperluas jangkauan pemasaran produk mereka. Hasil pre-test dan post-test menunjukkan adanya peningkatan signifikan pada pemahaman dasar teknologi informasi. Sebelum kegiatan, hanya 28% peserta yang memahami penggunaan perangkat digital untuk kegiatan bisnis; setelah pelatihan, tingkat pemahaman meningkat menjadi 78%.
Analisis Komparatif Algoritma Process Mining untuk Pemetaan Navigasi dan Deteksi Bottleneck E-Commerce Leiden Fauzi Yoka Surya; Lyvia Valentina; Zikri Firmansyah; Fathoni Fathoni; Ali Ibrahim
Jutisi : Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Vol 15, No 3 (2026): Juni 2026
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35889/jutisi.v15i3.3629

Abstract

E-commerce digitalization generates massive clickstream data, complicating customer journey mapping and bottleneck detection. The unstructured nature of web logs often leads to modeling failures. This study evaluates the performance of Alpha Miner, Heuristic Miner, and Inductive Miner algorithms in mapping user navigation routes and detecting interface inefficiencies using a public e-commerce clickstream dataset. Through Token-Based Replay evaluation, the research shows that Alpha Miner is inefficient for dynamic data and prone to Out of Memory errors. Conversely, Inductive Miner proved superior with a perfect fitness level (0.999), while Heuristic Miner was optimal in filtering noise (0.887). Further evaluations using the Performance Directly-Follows Graph localized the main bottleneck at the post-login transition to the shopping cart addition, which took the longest interface delay (42 minutes). These empirical findings provide a benchmark to optimize user interfaces and boost digital transaction conversions.Keywords: Bottleneck; Conformance Checking; Customer Journey; E-Commerce; Process Mining. AbstrakDigitalisasi e-commerce menghasilkan data clickstream masif yang menyulitkan pemetaan customer journey dan deteksi bottleneck. Sifat log web yang tidak terstruktur sering kali memicu kegagalan pemodelan. Penelitian ini mengevaluasi kinerja Alpha Miner, Heuristic Miner, dan Inductive Miner untuk memetakan navigasi pengguna serta mendeteksi inefisiensi antarmuka menggunakan dataset publik rekaman clickstream e-commerce. Melalui evaluasi Token-Based Replay, penelitian menunjukkan bahwa Alpha Miner tidak efisien untuk data dinamis dan rentan memicu Out of Memory. Sebaliknya, Inductive Miner terbukti paling unggul dengan tingkat kecocokan sempurna (0.999), sedangkan Heuristic Miner optimal dalam menyaring derau (0.887). Evaluasi lanjutan berbasis Performance Directly-Follows Graph melokalisasi bottleneck utama pada transisi pasca-login menuju penambahan keranjang belanja dengan jeda waktu antarmuka terlama (42 menit). Temuan empiris ini menjadi acuan untuk mengoptimalkan rekayasa antarmuka pengguna demi mendongkrak konversi transaksi digital. 
Segmentasi Pelanggan E-Commerce Berbasis Integrasi Text Mining dan RFM untuk Deteksi Dini Churn Violin Juneyla Nandita; Juseia Wulandari; Apriyadi Apriyadi; Ali Ibrahim; Fathoni Fathoni
Building of Informatics, Technology and Science (BITS) Vol 8 No 1 (2026): June 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v8i1.9687

Abstract

The growth of transactions on e-commerce platforms generates a massive volume of unstructured customer review data. However, traditional Customer Relationship Management (CRM) models such as RFM often only focus on quantitative transaction data and ignore the emotional dimension contained in customer reviews. This study aims to analyze the relationship between purchase frequency and customer comment polarity through the integration of Text Mining and CRM Analytics approaches. The novelty offered is the development of a hybrid method that combines Lexicon Refinement-based sentiment extraction with the Random Forest algorithm to overcome rating bias in global e-commerce platform data (Kaggle). The proposed method includes the use of Natural Language Processing (NLP) techniques, topic modeling based on Latent Dirichlet Allocation (LDA), and sentiment analysis to extract polarity scores. The test results show that the initial lexicon model has limitations with an accuracy of 52.14% due to noise in neutral reviews (3-star rating). However, after optimization using the Random Forest algorithm and neutral data filtering, the classification accuracy increased significantly to 74.62%. These results prove that sentiment integration is able to provide more accurate loyalty mapping and help e-commerce management detect potential churn in the At-Risk customer segment.
Development of a Flask-based Application for Bank Customer Churn Prediction as a Decision Support Tool Suluh Arif Wibowo; Muhammad Rezky; Ali Ibrahim; Mira Afrina; Fathoni Fathoni
Sistemasi: Jurnal Sistem Informasi Vol 15, No 4 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v15i4.6257

Abstract

Customer churn prediction is a crucial aspect of the banking industry for maintaining customer loyalty and reducing the cost of acquiring new customers. This study aims to develop a web-based decision support system capable of predicting potential customer churn using the Gradient Boosting Machine (GBM) algorithm. The dataset used is the Bank Customer Churn Dataset, consisting of 10,000 customer records with 14 attributes. The research stages include exploratory data analysis and preprocessing, which involves data cleaning, categorical feature encoding, feature engineering (BalanceSalaryRatio, TenureByAge, CreditScoreGivenAge), and data balancing using SMOTE to address class imbalance. The GBM model was trained on the balanced dataset and evaluated using accuracy, precision, recall, and F1-score metrics. The evaluation results show that the model achieved an accuracy of 83.95%, with a recall of 67.32% for the churn class, indicating a strong capability in identifying customers at risk of churn. Feature importance analysis reveals that Age and NumOfProducts are the most dominant features, contributing approximately 77% to the prediction. The model was then implemented in a Flask-based web application with an HTML and CSS interface, enabling non-technical users to perform real-time churn predictions. This system is expected to assist banking institutions in designing more targeted and data-driven customer retention strategies.
Co-Authors Abdiansah, Abdiansah Ahmad Fali Oklilas Ahmad Rafid Rizqullah Ahmad Rifai Al Farissi Ali Ibrahim Alisia Silver Stone Allsela Meiriza, Allsela Alzaini, Akbar Apriansyah Putra Apriansyah Putra Apriyadi Apriyadi Ari Wedhasmara Dedy Kurniawan Endang Lestari Ruskan Ermatita - Erwin, Erwin Juseia Wulandari Ken Ditha Tania Leiden Fauzi Yoka Surya Lyvia Valentina M Ihsan Jambak Maharani Maharani Mira Afrina Muhammad Rezky Naretha Kawadha Pasemah Gumay Pacu Putra Purwita Sari Rahmat Fadli Isnanto Rahmat Izwan Heroza Rezqe, Beriadi Agung Nur Rio Abdi Sucipta Rizka Dhini Kurnia Sanjaya, M. Rudi Siti Nur Arafah Sultan Arba'i Suluh Arif Wibowo Syamsuryadi Syamsuryadi Tika Octri Dieni Violin Juneyla Nandita Yadi Utama Yadi Utama Zikri Firmansyah