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All Journal Pastura; Journal Of Tropical Forage Science Jurnal Ilmu Perairan (Aquatic Science) Proceeding Buffalo International Conference JURNAL PETERNAKAN JUSIFO : Jurnal Sistem Informasi Sistemasi: Jurnal Sistem Informasi JURNAL MEDIA INFORMATIKA BUDIDARMA Tropical Animal Science Journal JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI JURNAL INSTEK (Informatika Sains dan Teknologi) Jurnal Teknologi Sistem Informasi dan Aplikasi Jurnal Sistem Cerdas JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Building of Informatics, Technology and Science JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Journal of Computer System and Informatics (JoSYC) Jurnal Teknik Informatika (JUTIF) Wahana Peternakan PETERPAN (Jurnal Peternakan Terapan) Jurnal Pendidikan dan Teknologi Indonesia Studium: Jurnal Pengabdian Kepada Masyarakat Malcom: Indonesian Journal of Machine Learning and Computer Science Jurnal Karya Abdi Masyarakat Jurnal Ilmiah Ilmu-Ilmu Peternakan Innovative: Journal Of Social Science Research Journal of Animal Nutrition and Production Science The Indonesian Journal of Computer Science Jurnal Inovasi dan Teknologi Pembelajaran
M. Afdal
Fakultas Peternakan Universitas Jambi
Religion Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Public Health Social Sciences Veterinary Other

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Pengenalan Pakan Blok Berbasiskan Dekanter Sawit Sebagai Pakan Ternak Ruminansia Di Desa Kota Baru, Kecamatan Geragai Kabupaten Tanjung Jabung Timur Afdal, M.; Kaswari, Teja; Fakhri, Saitul; Suryani, Heni
Jurnal Karya Abdi Masyarakat Vol. 4 No. 3 (2020): Jurnal Karya Abdi Masyarakat
Publisher : LPPM Universitas Jambi

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (376.663 KB) | DOI: 10.22437/jkam.v4i3.11304

Abstract

Tujuan dari kegiatan pengabdian kepada masyarakat ini adalah pemanfaatan dan pengenalan Dekanter Sawit (DS) kepada masyarakat peternak di desa Kotabaru. Pelaksanaan kegiatan ini adalah dengan memperkenalkan DS kepada anggota Kelompok Tani. Metoda yang dipergunakan adalah dengan survey pendahuluan terhadap potensi dan pemanfaatan DS di desa Kotabaru. Berdasarkan hasil survey pendahulaun ini maka diadakan sosialisai penggunaan DS sebagai pakan alternatif bagi ternak dengan program penyuluhan dan dilanjutkan dengan demonstrasi. Pada tahap awal ini diperkenalkan tata cara pembuatan pakan blok berbasiskan DS sebagai pakan alternatif untuk ternak ruminansia. Kesompulan dari program ini kelompok tani Suka Maju dapat menerima inovasi ini dengan memanfaatkan DS sebagai pakan alternatif ternak sapi dan sudah diadakan pelatihan pembuatan pakan blok berbasiskan DS
Analysis of User Adaptation to the My Capella Application based on the Coping Model of User Adaptation (CMUA) Mutia, Risma; Megawati, Megawati; Afdal, M.; Permana, Inggih
Sistemasi: Jurnal Sistem Informasi Vol 14, No 4 (2025): 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.v14i4.5328

Abstract

The My Capella application developed by PT Capella Dinamik Nusantara was designed to facilitate customer access to digital services, particularly for booking Honda motorcycle servicing. However, its use still encounters several challenges, especially regarding user adaptation. These include difficulties in understanding and utilizing features, a complex interface, and insufficient user guidance. This study aims to analyze and identify user adaptation behavior toward the My Capella application in the Pekanbaru area using the Coping Model of User Adaptation (CMUA), which evaluates how users respond to new technologies through cognitive and emotional processes. The research findings support four accepted hypotheses: opportunity appraisal significantly influences problem-focused adaptation; secondary appraisal significantly influences both problem-focused and emotion-focused adaptation; and threat appraisal significantly influences problem-focused adaptation. The strongest effect was observed in the relationship between secondary appraisal and problem-focused adaptation, with a t-statistic of 7.259 > 1.960. These findings indicate that users respond to the My Capella application both cognitively and emotionally, aligning with the CMUA framework and reflecting adaptation processes that are both problem-focused and emotion-focused. Therefore, it is recommended that application developers provide interactive training modules, regular outreach or user engagement sessions, and improvements to the user interface (UI/UX) design to make it more intuitive. These efforts can enhance users' understanding and comfort in using application features—especially during system updates.
IMPLEMENTASI DATA MINING DALAM PENCARIAN DAERAH STRATEGIS UNTUK PENGENALAN SEKOLAH SWASTA DENGAN METODE FP-GROWTH Afdal, M
Jurnal INSTEK (Informatika Sains dan Teknologi) Vol 3 No 2 (2018): OCTOBER
Publisher : Department of Informatics Engineering, Faculty of Science and Technology, Universitas Islam Negeri Alauddin, Makassar, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (2777.239 KB) | DOI: 10.24252/instek.v3i2.6044

Abstract

Sekolah Menengah Kejuruan (SMK) adalah salah satu institusi pendidikan resmi yang disahkan oleh pemerintah. Berdasarkan data pokok Sekolah Menengah Kejuruan untuk dinas pendidikan Kota Pekanbaru tahun 2016 terdapat sebanyak 60 Sekolah yang terdiri dari 7 SMK Negeri dan 53 SMK Swasta. Persaingan di dalam dunia bisnis, khususnya dalam bidang pendidikan pada SMK membuat pihak sekolah harus mencari pola sasaran daerah yang strategis dalam pengenalan sekolah. Dengan semakin banyaknya SMK Swasta di Kota Pekanbaru, membuat setiap sekolah berusaha mencari calon siswa baru kedaerah-daerah yang potensial. Salah satu cara yang dapat dilakukan untuk penentuan daerah strategis adalah dengan memanfaatkan teknik Data Mining. Dari data-data siswa yang ada disekolah dapat diolah mengunakan algoritma FP-Growth sehingga menghasilkan Frequent Itemset  yang menjadi informasi baru untuk  dimanfaatkan oleh sekolah dalam menentukan daerah yang strategis. Dalam penelitian ini yang menggunakan data siswa kelas X dengan  nilai minimum support = 0.04 dan nilai minimum confidence = 0.68 dinyatakan bahwa siswa yang berasal dari kecamatan Payung Sekaki adalah daerah yang paling strategis dalam pengenalan sekolah dengan tingkat kepercayaan 100% dan didukung oleh 4.7% dari data keseluruhan dengan nilai lift ratio 1.472. Kata Kunci: Association Rule, Data Mining, FP-Growth, Frequent Itemset
Sistem Pendukung Keputusan Pemilihan Supplier Menggunakan Metode Simple Additive Weighting Pada Toko Grosir Dua Putri Mawaddah, Zuriatul; Salisah, Febi Nur; Saputra, Eki; Afdal, M.
Jurnal Pendidikan dan Teknologi Indonesia Vol 5 No 8 (2025): JPTI - Agustus 2025
Publisher : CV Infinite Corporation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jpti.958

Abstract

Pemilihan supplier yang tepat memegang peranan penting dalam menjaga efisiensi operasional dan daya saing perusahaan, khususnya dalam bisnis grosir. Toko Grosir Dua Putri mengalami kesulitan dalam menentukan supplier terbaik secara objektif. Penelitian ini bertujuan untuk mengembangkan sistem pendukung keputusan (SPK) berbasis web menggunakan metode Simple Additive Weighting (SAW) guna mendukung pemilihan supplier secara efektif dan transparan. Metode SAW dipilih karena kemampuannya dalam memberikan penilaian terukur berdasarkan pembobotan beberapa kriteria, seperti harga, kualitas, ketepatan pengiriman, tempo pembayaran, dan layanan purna jual. Sistem ini dibangun menggunakan PHP dan MySQL. Evaluasi dilakukan melalui Black Box Testing dan User Acceptance Test (UAT), yang menunjukkan bahwa sistem bekerja dengan baik, dengan tingkat kepuasan pengguna sebesar 97,5%. SPK yang dikembangkan mampu memberikan rekomendasi supplier secara objektif, sehingga dapat meningkatkan akurasi dan efisiensi dalam pengambilan keputusan.
Analisis Sentimen Masyarakat Menggunakan Algoritma Long Short Term Memory (LSTM) Pada Ulasan Aplikasi Halodoc Yulianti, Nelvi; Afdal, M; Jazman, Muhammad; Megawati, Megawati; Anofrizen, Anofrizen
Building of Informatics, Technology and Science (BITS) Vol 7 No 2 (2025): September 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i2.7243

Abstract

Halodoc is a digital healthcare platform that provides users with convenient access to medical services online. This study aims to analyze public sentiment toward the Halodoc application based on 1,416 user reviews collected during the period from July to September 2024. The reviews are categorized into three sentiment classes: positive, negative, and neutral, using the Long Short-Term Memory (LSTM) algorithm. Prior to classification, the Word2Vec technique is applied to transform the words in the reviews into numerical vector representations for processing by the model. The analysis revealed that a portion of the reviews expressed negative sentiments, mainly concerning delays in medication delivery and slow responses from customer service. Model performance evaluation shows that the implementation of the LSTM algorithm optimized with the Adam (Adaptive Moment Estimation) optimizer and a dropout rate of 0.2 achieved the highest accuracy of 89.40% and an F1-score of 88.63%. These results indicate that the model performs very well in classifying sentiments and can be used as a useful tool for understanding user satisfaction with the Halodoc application.
Penerapan Support Vector Machine untuk Analisis Sentimen Pengguna X terhadap IndiHome, Biznet, dan Starlink Alfian, Zhevin; Afdal, M; Novita, Rice; Zarnelly, Zarnelly
Building of Informatics, Technology and Science (BITS) Vol 7 No 2 (2025): September 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i2.7429

Abstract

This study aims to analyze user sentiment on the social media platform X toward three major internet service providers in Indonesia, IndiHome, Biznet, and Starlink. The analysis focuses on five key variables: internet speed, network stability, pricing and service packages, customer service quality, and coverage availability. A total of 4,500 data points were collected through data crawling, then processed using text mining techniques and the Support Vector Machine (SVM) algorithm, with data imbalance addressed through the Random Oversampling method. Evaluation results show that IndiHome consistently demonstrated the best performance, achieving an accuracy of up to 90% in the customer service quality variable, and an overall average accuracy above 85% across all variables. Biznet generally ranked second, with accuracy ranging from 63% to 80%. Starlink placed lowest overall, although it still recorded competitive results, such as 82% accuracy in the internet speed variable. The application of Random Oversampling improved the model’s classification accuracy by an average of 6–12% compared to the non-oversampling model. This study offers strategic insights into public perception of internet services and can serve as a reference for improving service quality based on data-driven user feedback.
Analisis Sentimen Masyarakat Terhadap Kebijakan Ekspor Pasir Laut Berdasarkan Ulasan Twitter Menggunakan Algoritma Naive Bayes dan Support Vector Machine Zarqani, Zarqani; Afdal, M; Novita, Rice; Megawati, Megawati
Building of Informatics, Technology and Science (BITS) Vol 7 No 2 (2025): September 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i1.7431

Abstract

The export of sea sand has been banned since 2003 through a Decree of the Minister of Industry and Trade. However, on May 15, 2023, President Joko Widodo once again allowed the export of sea sand through Government Regulation No. 26 of 2023. This policy sparked controversy and went viral on social media, including on Twitter. This study aims to analyze public sentiment toward the policy based on reviews on Twitter using the Naïve Bayes and Support Vector Machine (SVM) algorithms. Data was collected through crawling techniques, then processed using text preprocessing methods, word weighting using TF-IDF, and random oversampling to balance the data. The data was then categorized into four thematic variables—economy, environment, social, and geological policy—to examine a more focused distribution of sentiment. Analysis of 2,765 data points revealed that the majority of sentiment was negative (55%), indicating public opposition to the sea sand export policy, followed by neutral sentiment (30%) and positive sentiment (15%). Performance evaluation shows that SVM excels in the Economy category with nearly 95% accuracy, while in other categories the difference with Naïve Bayes is relatively small. This study is expected to provide insights into the Indonesian public's perception of the sea sand export policy and its implications across various sectors.
Analisis Sentimen Terhadap Pemain Naturalisasi dan Lokal Tim Nasional Sepakbola Indonesia Menggunakan Support Vector Machine Arrazak, Fadlan; Afdal, M; Novita, Rice; Megawati, Megawati
Building of Informatics, Technology and Science (BITS) Vol 7 No 2 (2025): September 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i2.7471

Abstract

The inclusion of naturalized players in Indonesia's national football team has sparked diverse public reactions, particularly on social media platforms like Twitter. This study aims to compare public opinion toward naturalized and local players through sentiment analysis. A total of 2,342 tweets were categorized into three sentiment classes: positive, neutral, and negative. Naturalized players received a higher number of positive sentiments, totaling 809, compared to 333 negative and 231 neutral sentiments. In contrast, local players gained 465 positive sentiments, 317 negative, and 187 neutral, indicating a generally more favorable perception of naturalized players among the public. Further analysis was conducted using the Support Vector Machine (SVM) classification algorithm along with the SMOTE technique for data balancing, focusing on five key aspects: performance, experience, physical condition, adaptability, and communication. The classification results showed that naturalized players outperformed in physical condition with an accuracy of 96 percent, followed by performance and adaptability, each at 90 percent. On the other hand, local players showed superiority only in communication with an accuracy of 92 percent. In terms of precision and recall, naturalized players again led in physical condition, achieving 97 percent precision and 96 percent recall, while local players excelled in communication with both precision and recall at 92 percent. These findings offer valuable insights for policymakers and football organizations in formulating more effective naturalization strategies.
Analisis Sentimen Masyarakat Terhadap Kebocoran Pusat Data Nasional Sementara Menggunakan Algoritma Random Forest dan Support Vector Machine Basri, Faishal Khairi; Afdal, M; Angraini, Angraini; Rozanda, Nesdi Evrilyan
Building of Informatics, Technology and Science (BITS) Vol 7 No 2 (2025): September 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i2.7473

Abstract

A ransomware attack on Indonesia’s Temporary National Data Center (PDNS) in June 2024 triggered major public concern over data security and government preparedness. This study aims to analyze public sentiment toward the incident using an Aspect-Based Sentiment Analysis approach on 2,700 Indonesian-language tweets collected from the X platform. The research follows the SEMMA (Sample, Explore, Modify, Model, Assess) methodology, involving text preprocessing, aspect extraction using part-of-speech tagging and named entity recognition, feature representation using Term Frequency-Inverse Document Frequency, and aspect refinement through semantic coherence. Extracted aspects are grouped into five categories: data security, institutions, infrastructure, politics and economy, and impact. Sentiment classification is carried out using the IndoBERTweet model. Results indicate a strong dominance of negative sentiment, particularly in the infrastructure and institutional categories, with no positive sentiment recorded in the political and economic aspect. To address class imbalance in sentiment distribution, the Synthetic Minority Oversampling Technique is applied during model training. Performance evaluation of two algorithms—Random Forest and Support Vector Machine—shows that Random Forest performs best, achieving 96% accuracy on a 70:30 data split and 99.05% average accuracy using 10-fold cross-validation. These findings highlight the effectiveness of aspect-based sentiment analysis and demonstrate Random Forest's superiority in handling imbalanced sentiment classification tasks.
Analisa Sentimen Pengguna Aplikasi DANA Pada Ulasan Google Play Store Menggunakan Algoritma Naive Bayes Classifier dan K-Nearest Neighbors Sabillah, Dian Ayu; Afdal, M; Permana, Inggih; Muttakin, Fitriani
Building of Informatics, Technology and Science (BITS) Vol 7 No 2 (2025): September 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i2.7861

Abstract

The use of digital wallets such as DANA in Indonesia continues to increase along with the need for fast and practical non-cash transactions. User reviews on the Google Play Store are an important source of information to assess satisfaction and service problems. This study aims to classify user sentiment towards the DANA application using the Naïve Bayes Classifier (NBC) and K-Nearest Neighbor (KNN) algorithms. A total of 1,000 reviews were collected and processed through text cleaning, tokenization, stopword removal, and stemming. Sentiments were classified into positive, neutral, and negative using the lexicon method and expert validation. The results showed that NBC was superior to KNN, with the highest accuracy of 71.83%, while KNN only reached 56.44%. NBC was also more effective in detecting negative sentiment, although both were less than optimal for neutral sentiment. Word cloud visualization displays the dominant words in each sentiment category. The conclusion of this study states that Naïve Bayes is more effective in analyzing sentiment reviews of digital wallet applications such as DANA.
Co-Authors - Mardalena, - A. Adriani AA Sudharmawan, AA Addion Nizori ADRIANI ADRIANI Adriani Adriani Afandi, Rival Aini, Delvi Nur Al-Yasir, Al-Yasir Alfakhri, Rezky Alfian, Zhevin Amrullah Amrullah Andriyani, Dwi Ratna Angraini Angraini Anisa Putri Annisa Ramadhani Anofrizen Anofrizen Arif Marsal Arrazak, Fadlan Auliani, Sephia Nazwa Ayu Lestari Silaban Ayu Silaban Azzahra, Aura Basri, Faishal Khairi Darlis Darlis Darlis Darlis, Darlis Eki Saputra F. Safiesza, Qhairani Frilla Fauzan Ramadhan Febi Nur Salisah Filawati Filawati FITRY TAFZI Hendri, Desvita Heni Suryani Husaini, Fahri Husna, Nur Alfa Indriyani Indriyani Indriyani Indriyani Inggih Permana Intan, Sofia Fulvi Irwanda, Mahyuda Jazman, Muhammad Kusuma, Gathot Hanyokro Lisani Lisna, Lisna Loka, Septi Kenia Pita Luber, Yusuf Amirullah Mawaddah, Zuriatul Megawati - Miftahul Jannah Mochammad Imron Awalludin Mona Fronita, Mona Muhammad Ambar Islahuddin Munandar, Darwin Munzir, Medyantiwi Rahmawita Mustakim Mustakim Mustakim Mutia, Risma Muttakin, Fitriani Nabillah, Putri Nasution, Nur Shabrina Nelwida Nelwida Nurfadilla, Nadia Nurkholis Nurkholis Pertiwi, Tata Ayunita Prizky Nanda Mawaddah Putra, Moh Azlan Shah Putri, Celine Mutiara Putri, Suci Maharani Rahayu Suseno Rahmah, Astriana Rahmawita M, Medyantiwi Rahmawita, Medyantiwi Ramadani, Faradila Ramadhani, Indah Rayean, Rival Valentino Remon Lapisa Rice Novita Rizna, Gebby Rozanda, Nesdi Evrilyan Saad, Wan Zuhainis Sabillah, Dian Ayu Saitul Fakhri Sari, Gusmelia Puspita Sarwo Edy Wibowo Silaban, Ayu Siswahyudianto Siti Monalisa Siti Rohimah Suhessy Syarif Suhessy Syarif, Suhessy Suryadi Suryadi Suryani, Heni Susanti, Pingki Muliya Suseno, Rahayu Syafi'i, Azis Syafrizal Syafrizal Syahri, Alfi T. T. Poy Teja Kaswari Tri Astuti Triningsih, Elsa Tshamaroh, Muthia Ula, Walid Alma Wibisono, Yudistira Arya Wilrose, Anandeanivha Y Zaharanova Yuda, Afi Ghufran Yulianti, Nelvi Yun Alwi Yurleni Yurleni Yusuf Amirullah Luber Zarnelly Zarnelly Zarqani, Zarqani