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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 Building of Informatics, Technology and Science Jurnal Inovasi dan Teknologi Pembelajaran (JINOTEP) Kajian dan Riset Dalam Teknologi Pembelajaran 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
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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Perancangan dan Analisis Kompor Oli Berbasis Tekanan Uap untuk Meningkatkan Intensitas Api Afdal, M.; Lapisa, Remon
Innovative: Journal Of Social Science Research Vol. 4 No. 4 (2024): Innovative: Journal Of Social Science Research
Publisher : Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/innovative.v4i4.14839

Abstract

Penelitian ini bertujuan untuk merancang dan menganalisis kompor oli berbasis tekanan uap guna meningkatkan intensitas api. Kompor oli ini menggunakan oli bekas sebagai bahan bakar yang diolah melalui sistem tekanan uap untuk menghasilkan pembakaran yang lebih efisien dan bersih. Metode yang digunakan meliputi desain kompor, pembuatan prototipe, dan pengujian performa. Hasil penelitian menunjukkan bahwa dengan memanfaatkan tekanan uap, kompor oli ini mampu meningkatkan intensitas api dan mengurangi emisi yang dihasilkan. Implementasi dari hasil penelitian ini diharapkan dapat menjadi solusi alternatif untuk memanfaatkan oli bekas secara lebih efisien dan ramah lingkungan.
Prediction Of Andesit Stone Production using Support Vector Regression Algorithmression Azzahra, Aura; Afdal, M.; Mustakim, Mustakim; Novita, Rice
Sistemasi: Jurnal Sistem Informasi Vol 13, No 5 (2024): 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.v13i5.4155

Abstract

PT. Atika Tunggal Mandiri is a company engaged in andesite stone mining located in the fifty municipalities, West Sumatra. The demand for andesite stones in the company continues to increase, necessitating an increase in production to meet it. Therefore, accurate prediction is needed to assist effective operational planning, enabling the estimation of future andesite stone production to meet market demand. This study aims to predict andesite stone production using the Machine Learning method, specifically the Support Vector Regression algorithm. The research utilizes data from January 2022 to November 2023 with an 80%:20% split for training and testing data. The experimental results using the Linear Kernel yielded an RMSE value of 3444.12 and an MAPE of 9.27%, categorized as "Very Good," followed by the RBF kernel and Polynomial kernel. Based on the obtained error results, the Support Vector Regression algorithm is the best algorithm for predicting andesite stone production.
A Comparison of K-Means and Fuzzy C-Means Clustering Algorithms for Clustering the Spread of Tuberculosis (TB) in the Lungs Ramadani, Faradila; Afdal, M.; Mustakim, Mustakim; Novita, Rice
Sistemasi: Jurnal Sistem Informasi Vol 13, No 5 (2024): 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.v13i5.4277

Abstract

Tuberculosis (TB) is an airborne infectious disease that affects people of all ages, including infants, children, teenagers and the elderly. This disease is prevalent in different areas of Indragiri Hilir Regency, so it is important to identify and group the areas that are the focus of its spread. The purpose of this study is to help hospitals organize training in areas where tuberculosis is common. This study uses a data mining method with grouping techniques of K-Means and Fuzzy C-Means algorithms based on patient data from Puri Husada Tembilahan Hospital from 2020 to 2023. After several experiments, the results were evaluated with DBI, which showed that K- Means gave the best validity with a value of 0.9146. Which shows that the areas with high risk of TB are Tembilahans aged 55-64 who have been diagnosed with complicated TB. This method was then applied to the TB group information system of Puri Husada Tembilahan District Hospital in the hope that it could help the hospital reduce the spread of the disease in the affected area.Keywords: DBI, fuzzy c-means, clustering, k-means, tuberculosis.
Analisis Sentimen Ulasan Aplikasi PLN Mobile Menggunakan Algoritma Naïve Bayes Classifier dan K-Nearest Neighbor: Sentiment Analysis of PLN Mobile Application Review Using Naïve Bayes Classifier and K-Nearest Neighbor Algorithm Syafrizal, Syafrizal; Afdal, M.; Novita, Rice
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 4 No. 1 (2024): MALCOM January 2024
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v4i1.983

Abstract

Bukti nyata PLN terus meningkatkan pelayanannya adalah dengan meluncurkan sebuah aplikasi yaitu PLN Mobile. Banyak pelanggan yang merasakan kemudahan dengan adanya aplikasi tersebut. Namun kini beberapa pelanggan mulai menjumpai permasalahan seperti gagal memuat lokasi saat melakukan pengaduan dan saat pembelian token dengan virtual account, saldo telah terpotong namun kode token tidak muncul. Penelitian ini melakukan analisis sentimen terhadap ulasan pengguna aplikasi PLN Mobile menggunakan pendekatan text mining. Pendekatan ini dapat melakukan klasifikasi sentimen pada ulasan pengguna dengan cepat. Data dikumpulkan menggunakan teknik scrapping pada Google Play Store dan mendapatkan 3000 baris data. Data tersebut kemudian diberi label oleh seorang pakar sehingga menghasilkan 2099 sentimen positif (69,97%), 368 netral (12,27%) dan 533 negatif (17,77%). Selanjutnya dilakukan pemodelan menggunakan algoritma NBC dan KNN dengan K-Fold Cross Validation sebagai teknik validasi. Hasilnya menunjukkan model NBC lebih baik dibandingkan KNN dengan akurasi sebesar 77,69%, recall 53,14%, precision 59,84% dan F1-Score 54,09%. Selanjutnya proses analisis dilakukan dengan visualisasi data menggunakan word cloud. Hasilnya yaitu dengan adanya aplikasi PLN Mobile memberikan kemudahan kepada pelanggan dalam menggunakan layanan PLN seperti pembelian token, pengaduan, dan berbagai fitur lainnya. Namun aplikasi PLN Mobile masih memiliki beberapa permasalahan yang sering menjadi ulasan penggunanya salah satunya adalah saat melakukan pembayaran token.
Analisis Loyalitas Pelanggan Business To Business Berdasarkan Model RFM Menggunakan Algoritma Fuzzy C-Means: Business to Business Customer Loyalty Analysis Based on RFM Model Using Fuzzy C-Means Algorithm Al-Yasir, Al-Yasir; Afdal, M.; Zarnelly, Zarnelly; Marsal, Arif
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 4 No. 1 (2024): MALCOM January 2024
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v4i1.1163

Abstract

PT. XYZ merupakan perusahaan yang bergerak di bidang distributor atap plastik dan Aluminium Composit Panel (ACP) yang mengadopsi model usaha B2B. Saat ini strategi yang digunakan oleh PT. XYZ masih belum berfokus pada segmentasi pelanggan dan masih memperlakukan setiap pelanggan dengan sama. Selain itu data penjualan yang terdapat ribuan lebih riwayat transaksi hanya digunakan sebagai arsip yang seharusnya dapat digunakan untuk pengembangan strategi perusahaan. Berdasarkan hal tersebut, penelitian ini melakukan segmentasi pelanggan pada PT. XYZ menggunakan model RFM dan algoritma FCM untuk menganalisis pelanggan bersasarkan karakteristik dan perilakunya. Data yang digunakan terdiri dari 9163 transaksi yang memuat 494 pelanggan. Untuk mendapatkan jumlah cluster yang optimal maka dilakukan pengujian pada jumlah cluster yaitu 2-10. Hasilnya menunjukkan 2 cluster sebagai jumlah yang terbaik dengan nilai DBI 0,4908. Cluster 1 yang terdiri dari 387 pelanggan dikategorikan sebagai loyal customer sedangkan cluster 2 yang terdiri dari 107 pelanggan dikategorikan sebagai lost customer. Sebagai pelanggan yang loyal, perusahaan perlu memberikan apresiasi untuk mempertahankan hubungan baik dengan pelanggan seperti memberikan diskon, ataupun penawaran khusus. Kemudian untuk segmen lost customer, perusahaan perlu mengambil langkah yang tepat untuk mencoba memulihkan hubungan dengan pelanggan dan menganalisis faktor dan penyebab pelanggan pada segmen ini beralih ke perusahaan lain.
Penerapan Algoritma Long Short-Term Memory untuk Prediksi Produksi Kelapa Sawit: Application of Long Short-Term Memory Algorithm for Palm Oil Production Prediction Husaini, Fahri; Permana, Inggih; Afdal, M.; Salisah, Febi Nur
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 4 No. 2 (2024): MALCOM April 2024
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v4i2.1187

Abstract

Kelapa sawit memberikan kontribusi yang besar bagi perkembangan perekonomian Indonesia. Salah satunya ekspor non migas negara dan yang terus mengalami pertumbuhan yang dilakukan perusahaan kelapa sawit. PT XYZ merupakan salah satu perusahaan kelapa sawit yang mengolah kelapa sawit menjadi minyak kelapa sawit. Dalam menghadapi permintaan minyak kelapa sawit dunia yang terus meningkat, PT. XYZ berkomitmen untuk meningkatkan produksinya. Untuk meningkatkan produksi, PT XYZ telah menetapkan target produksi dengan melakukan prediksi produksi kelapa sawit menggunakan metode Global Telling. Namun, metode ini kurang efektif karena tidak dilakukan secara berkala. Untuk itu, diperlukan suatu metode yang dapat mempelajari pola panen setiap bulannya untuk membuat target produksi. Penelitian ini menerapkan Algoritma Long Short-Term Memory dengan percobaan beberapa parameter untuk menemukan model terbaik yang dapat memprediksi produksi kelapa sawit secara akurat. Berdasarkan hasil percobaan, model dengan optimizer RMSprop, learning rate 0.001, dan batch size 8 merupakan model dengan parameter terbaik dengan nilai RMSE 0.1725, MAPE 0.5087, dan R2 0.0578. Model tersebut memprediksi bahwa produksi kelapa sawit akan mengalami penurunan
Analisis Penerimaan Pengguna E-Wallet DANA Menggunakan Metode TAM dan Delone Mclean Sari, Gusmelia Puspita; Salisah, Febi Nur; Rozanda, Nesdi Evrilyan; Afdal, M; Jazman, Muhammad; Marsal, Arif
Journal of Information System Research (JOSH) Vol 5 No 4 (2024): Juli 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v5i4.5334

Abstract

The industrial revolution 4.0 motivates advances in information technology through the idea of ​​the internet of things (IoT). One form of implementing the internet of things is the use of e-wallets as a payment medium. One e-wallet that is popular among users is the DANA application. The DANA application helps users make non-cash or cardless payments, making transactions easier and more practical. Despite the advantages offered, there are several problems in its implementation, such as delays when making transfers, not being able to top up and losing balance. Therefore, using the TAM and Delone Mclean approach, this research aims to analyze user acceptance of the DANA application as an effort to see what factors make the DANA application able to be accepted and used by many users. This research was conducted on DANA application users who live in Pekanbaru City with a sample size of 100 respondents. The research uses quantitative methods by distributing questionnaires online. The data that has been collected is processed first using Microsoft Excel, then continued using SmartPLS 4 to analyze PLS-SEM. From hypothesis testing, the results obtained were that seven hypotheses were accepted and declared positive and significant, while one hypothesis was rejected because it did not show a significant relationship.
Analysis of Fiber Fractions in Corn Cobs Fermented with Phanerochaete chrysosporium Supplemented with Different Carbohydrate Sources Afdal, M; Astuti, Tri; Sari, Rica Mega
Journal of Animal Nutrition and Production Science Vol. 3 No. 01 (2024): Journal of Animal Nutrition and Production Science
Publisher : Department of Agriculture

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36665/janaps.v3i01.697

Abstract

This study aimed to evaluate the content of Neutral Detergent Fiber (NDF), Acid Detergent Fiber (ADF), cellulose, and hemicellulose in corn cobs fermented with white rot fungi (Phanerochaete chrysosporium) and supplemented with various carbohydrate sources. A Completely Randomized Design was employed, consisting of four treatments with four replications each. The results revealed the highest NDF content in T3 (80.16%), followed by T2 (79.50%), T0 (77.30%), and T1 (73.63%). The highest ADF content was observed in T1 (47.08%), T0 (45.78%), T3 (44.85%), and T2 (43.13%). The highest cellulose content was recorded in T2 (17.51%), followed by T1 (15.32%), T0 (15.32%), and T3 (13.39%). For hemicellulose, the highest values were found in T2 (36.37%), followed by T3 (35.30%), T0 (31.51%), and T1 (26.54%). It can be concluded that fermenting corn cobs with P. chrysosporium and supplementing with different carbohydrate sources significantly (P<0.01) affected the fiber fractions (NDF, ADF, cellulose, and hemicellulose). Rice bran (T1) was most effective in reducing NDF and hemicellulose content, molasses (T2) in reducing ADF content, and tapioca flour (T3) in lowering cellulose content.
Integrating Support Vector Machines and Geospatial Analysis for Enhanced Tuberculosis Case Detection and Spatial Mapping Jannah, Miftahul; Jazman, Muhammad; Afdal, M; Megawati, Megawati
Building of Informatics, Technology and Science (BITS) Vol 7 No 1 (2025): June (2025)
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i1.7158

Abstract

Tuberculosis (TB) remains a significant global health problem, with Indonesia ranking third in the world in terms of TB burden. Riau Province recorded 13,007 notified TB cases in 2022 with a Case Notification Rate (CNR) of 138 per 100,000 population, still far from the national target. This study aims to develop a TB case classification system using Support Vector Machine (SVM) integrated with geospatial analysis to identify TB positive cases from screening data and visualize their spatial distribution in Riau Province. The research data was sourced from the Tuberculosis Information System (SITB) of the Riau Provincial Health Office for the period January-December 2024, covering 350 samples with demographic information, clinical symptoms, and patient risk factors. The research process includes data collection, preprocessing with Min-Max and Z-Score methods, feature extraction, modeling with SVM using various kernels (RBF, Linear, Polynomial, and Sigmoid), and geospatial visualization using Google Earth Engine (GEE). The results showed that the SVM model with Linear kernel achieved the highest accuracy of 80%, sensitivity of 100%, and specificity of 80% in detecting TB cases. Geospatial analysis successfully identified clusters of TB cases in several districts in Riau Province, with Pekanbaru City (112 cases) and Rokan Hulu (89 cases) as the main hotspots. The integration of machine learning and geospatial analysis proved effective in improving TB detection and providing a comprehensive understanding of disease spread patterns in Riau Province.
Perbandingan Performa Algoritma SVR, LSTM, dan SARIMA dalam Peramalan Produksi Kelapa Sawit Hendri, Desvita; Permana, Inggih; Salisah, Febi Nur; Afdal, M; Megawati, Megawati; Saputra, Eki
Building of Informatics, Technology and Science (BITS) Vol 7 No 1 (2025): June (2025)
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i1.7170

Abstract

Oil palm production in Indonesia fluctuates significantly due to various factors such as weather, soil fertility, and fruit bunch condition. These changes These changes have an impact on price stability, supply and planning for the palm oil industry. industry planning. Therefore, to improve decision-making in this industry, an accurate forecasting method is required to improve decision-making regarding distribution. appropriate decision-making regarding distribution. This study aims to compare the performance of three machine learning-based forecasting methods, namely Support Vector Regression (SVR), Long Short-Term Memory (LSTM), and Seasonal Autoregressive Integrated Moving Average (SARIMA), in predicting palm oil production based on historical data for the last 10 years obtained from PTPN V Riau. The evaluation results show that the SVR model with a linear kernel provides the best performance with an MSE value of 4.1718. with MSE 4.1718, RMSE 0.0020, MAE 0.0018, MAPE 0.2014% and R2 0.9988. The SVR model provides superior prediction results compared to LSTM and SARIMA. with LSTM and SARIMA in forecasting palm oil production. This research is expected to make a real contribution in the development of a more reliable prediction system, thus supporting operational efficiency and stability of the palm oil industry in Indonesia. stability of the palm oil industry in Indonesia.
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 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 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 Priady, Muhamad Ilham Prizky Nanda Mawaddah Putra, Moh Azlan Shah Putri, Celine Mutiara Putri, Suci Maharani Rahmah, Astriana Rahmawita, Medyantiwi Ramadani, Faradila Ramadhani, Indah Rayean, Rival Valentino Remon Lapisa Rice Novita Rozanda, Nesdi Evrilyan Saad, Wan Zuhainis Sabillah, Dian Ayu Saitul Fakhri Sari, Gusmelia Puspita Sarwo Edy Wibowo Siti Monalisa Siti Rohimah Suhessy Syarif Suhessy Syarif, Suhessy Suryadi Suryadi 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