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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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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.
Pengelompokkan Perguruan Tinggi di Indonesia Menggunakan Algoritma BIRCH Husna, Nur Alfa; Mustakim, Mustakim; Afdal, M; Rahmawita, Medyantiwi
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.v7i1.7234

Abstract

Accreditation is currently the main focus for all universities. Each institution strives to get superior accreditation. The evaluation and assessment process carried out by BAN-PT is based on data reported by universities to PDDikti. This research aims to assist universities in achieving superior accreditation, by providing recommendations regarding the most influential attributes and clustering to find patterns or data structures from PDDikti. This research uses two feature selection methods AHP and Chi-Square are used separately to identify the most influential attributes. The results of each method were used as input features for the clustering process using the BIRCH algorithm. The purpose of this approach is to evaluate the effect of feature selection from both methods on the quality of clustering results. The evaluation is done using the Davies-Bouldin Index (DBI) metric. The results showed that the Lecturer attribute has the highest eigenvalue in AHP which is 0.379, indicating its significant role in accreditation assessment. Meanwhile, the Year of Establishment Decree attribute has the highest Chi-Square value of 290.625 which indicates a strong correlation with accreditation results. In addition, based on the cluster DBI value, it shows that AHP is superior to chi-square, so AHP is considered more effective in this context. With the best Davies Bouldin Index (DBI) value of 0.73603 in cluster 7 with a threshold of 0.05 and a branching factor of 50.
Perbandingan Algoritma LSTM, Bi-LSTM, GRU, dan Bi-GRU untuk Prediksi Harga Saham Berbasis Deep Learning Tshamaroh, Muthia; Permana, Inggih; Salisah, Febi Nur; Muttakin, Fitriani; Afdal, M
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.v7i1.7252

Abstract

Stock price prediction is an important component in making investment decisions. This study aims to compare the performance of four deep learning models, namely LSTM, Bi-LSTM, GRU, and Bi-GRU, in predicting stock prices, in order to find the most optimal model for the implementation of an accurate stock price prediction system. Five years of historical data undergoes normalization, windowing, and is separated into training data, validation data, and test data. Model training is conducted with different settings of batch size, timestep, and three kinds of optimizers (Adam, SGD, RMSprop). Performance assessment employs MSE, RMSE, MAE, and R² measurements. The findings indicate that the Bi-GRU model utilizing Adam optimizer settings, a batch size of 8, and a timestep of 21 yields the highest performance, achieving an MSE of 0.0003, an RMSE of 0.0169, an MAE of 0.0129, and an R² of 0.9438. This model demonstrates a strong capability to identify intricate patterns and long-term temporal relationships, outperforming other models in accuracy. The results advocate for the establishment of a predictive system that aids investors and firms in making strategic decisions based on data.
Analisis Sentimen Masyarakat Terhadap Kebijakan IKN Pada Periode Jokowi dan Prabowo Menggunakan Algoritma NBC, SVM, dan K-NN Nasution, Nur Shabrina; Permana, Inggih; Salisah, Febi Nur; 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.v7i1.7276

Abstract

The relocation of the National Capital City (IKN) from Jakarta to East Kalimantan has generated a variety of responses from the Indonesian people recorded through social media, especially platform X. This study aims to analyze and compare public sentiment towards the IKN policy in two periods of government, namely President Joko Widodo and President Prabowo Subianto. This study aims to analyze and compare public sentiment towards the policy of the National Capital City during two periods of government, namely President Joko Widodo and President Prabowo Subianto, using a machine learning approach. The three algorithms used in sentiment classification are Naive Bayes Classifier (NBC), Support Vector Machine (SVM), and K-Nearest Neighbor (K-NN). The research process includes data crawling (600 data each per period), text preprocessing (cleaning, tokenizing, filtering, stemming), data labeling using Lexicon-Based approach with InSet dictionary, and weighting using TF-IDF method. The results of the analysis show that in the Jokowi period, public sentiment tends to be more balanced, with the dominance of negative sentiment (35.9%), followed by positive sentiment (33.4%) and neutral (30.7%). Whereas in the Prabowo period, negative sentiment increased to 40.3%, while positive decreased to 26.3%. Based on the model accuracy evaluation, in the Jokowi period, the NBC algorithm showed the best performance with an accuracy of 73%, while in the Prabowo period, the SVM algorithm excelled with the highest accuracy reaching 81%. These findings provide a dynamic picture of public perception of IKN policies under two different governments.
Analisis Sentimen Masyarakat Terhadap Liga Indonesia Menggunakan Algoritma Naïve Bayes Classifier dan Support Vertor Machine Pada Platform X dan YouTube Irwanda, Mahyuda; Afdal, M; Novita, Rice; Zarnelly, Zarnelly
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.v7i1.7294

Abstract

The Indonesian League is a national football competition that attracts a lot of public attention. However, various problems such as controversial referee decisions, fan riots, and match-fixing issues are often in the spotlight. This study aims to analyze public sentiment towards the Indonesian League using the Naïve Bayes Classifier (NBC) and Support Vector Machine (SVM) algorithms. Data were collected from social media platform X (Twitter) as many as 2000 tweets and YouTube as many as 2000 comments in the period from January 2023 to December 2024. After going through preprocessing stages such as cleaning, case folding, tokenizing, stopword removal, and stemming, the data was classified into positive, negative, and neutral sentiments. The results showed that SVM had a higher accuracy (99%) than NBC (85%) in sentiment analysis.
Penerapan Data Mining Untuk Analisis Sentimen Masyarakat Terhadap Ibu Kota Nusantara Pada Media Sosial X Rayean, Rival Valentino; Afdal, M; Permana, Inggih; Rozanda, Nesdi Evrilyan
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.v7i1.7318

Abstract

The policy of relocating the National Capital City to Nusantara (IKN) has become a viral and hotly debated issue in Indonesia, triggering diverse public reactions ranging from support to opposition. To understand the dynamics of this public sentiment, this research analyzed user responses from the social media platform X. A total of 1000 tweet data were collected, equally divided into 500 tweets before and 500 tweets after Indonesia's 2024 Independence Day ceremony. These tweet data were then manually labeled and classified for sentiment analysis using Naive Bayes and Random Forest data mining algorithms, with the SMOTE technique applied to address data class imbalance. The analysis results showed that before the Independence Day ceremony, sentiment towards the National Capital City to Nusantara (IKN) was dominated by 44% negative tweets (219 data points), followed by 30% positive (151 data points), and 26% neutral (130 data points). Post-ceremony, negative sentiment significantly increased to 50% (252 data points), while positive sentiment slightly rose to 33% (165 data points), and neutral sentiment decreased to 17% (83 data points). In model performance evaluation, the Random Forest algorithm demonstrated higher classification accuracy compared to Naive Bayes. Nevertheless, the accuracy difference between the two algorithms was relatively small, indicating that both were quite effective for sentiment analysis on this research dataset. This study successfully presents a comprehensive overview of the dynamics and polarity of public opinion on social media X regarding the ongoing policy of relocating the National Capital City to Nusantara.
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