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All Journal Proceeding Buffalo International Conference JUSIFO : Jurnal Sistem Informasi Sistemasi: Jurnal Sistem Informasi JURNAL MEDIA INFORMATIKA BUDIDARMA Jurnal Sistem Cerdas Building of Informatics, Technology and Science Jurnal Sistem informasi dan informatika (SIMIKA) Jurnal Inovasi dan Teknologi Pembelajaran (JINOTEP) Kajian dan Riset Dalam Teknologi Pembelajaran JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Jurnal Pendidikan dan Teknologi Indonesia Studium: Jurnal Pengabdian Kepada Masyarakat Malcom: Indonesian Journal of Machine Learning and Computer Science Jurnal Karya Abdi Masyarakat
M. Afdal
Faculty of Animal Husbandry Jambi University Kampus Mandala Darat Jambi 363611ndonesia
Religion Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Health Professions Public Health Social Sciences

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THE SENSORY PROPERTIES AND FLAVOR CHARACTERISTICS OF MEAT OF CATTLE AND BUFFALO FED PROTECTED LEMURU FISH (Bali sardinella) OIL AS DRIED CARBOXYLATE SALT MIXTURE (DCM) IN RATION Yurleni, -; Amri, Ulil; Mardalena, -; Afdal, M.
Proceeding Buffalo International Conference 2013
Publisher : Proceeding Buffalo International Conference

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

Abstract

The aim of the study was to reveal the sensory and characteristic differences between cattle and buffalo meats. There were 6 swamp buffalo and 8 Ongole cross cattle used in the study which arranged as a factorial experiment 2x2, based on a completely randomized design: 2 species and 2 level of DCM (dried carboxilated salt mixture; 0 and 45 g per kg ration). The result indicated that DCM in ration was significantly (P<0.05) strengthen the odor of the meat compared with control meat. Buffalo meat was significantly (P<0.05) darker than of cattle.
Pendekatan Machine Learning: Analisis Sentimen Masyarakat Terhadap Kendaraan Listrik Pada Sosial Media X Kusuma, Gathot Hanyokro; Permana, Inggih; Salisah, Febi Nur; Afdal, M.; Jazman, Muhammad; Marsal, Arif
JUSIFO : Jurnal Sistem Informasi Vol 9 No 2 (2023): JUSIFO (Jurnal Sistem Informasi) | December 2023
Publisher : Program Studi Sistem Informasi, Fakultas Sains dan Teknologi, Universitas Islam Negeri Raden Fatah Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.19109/jusifo.v9i2.21354

Abstract

Environmental issues and the depletion of fossil fuels continue to escalate as the number of fossil fuel-based vehicle users increases in Indonesia. Electric vehicles emerge as one of the potential alternative solutions to address current environmental challenges, given their eco-friendly nature and lack of pollution emissions. Sentiment analysis is conducted to understand public responses, both supportive and opposing, towards electric vehicles. This research aims to analyze the sentiment of X-social media users regarding electric vehicles using machine learning techniques. The research stages include data collection, data selection, preprocessing, and classification using Naïve Bayes Classifier (NBC), Support Vector Machine (SVM), and K-Nearest Neighbor (KNN) algorithms. The test results show that on a balanced dataset using ROS, SVM performs the best with accuracy = 68.7%, precision = 77.9%, and recall = 68.4%. Meanwhile, NBC yields an accuracy of 60.3%, precision of 61.3%, and recall of 60.3%, while KNN has an accuracy of 53.9%, precision of 54%, and recall of 53.9%.
Perbandingan Algoritma KNN, NBC, dan SVM: Analisis Sentimen Masyarakat Terhadap Perparkiran di Kota Pekanbaru Intan, Sofia Fulvi; Permana, Inggih; Salisah, Febi Nur; Afdal, M.; Muttakin, Fitriani
JUSIFO : Jurnal Sistem Informasi Vol 9 No 2 (2023): JUSIFO (Jurnal Sistem Informasi) | December 2023
Publisher : Program Studi Sistem Informasi, Fakultas Sains dan Teknologi, Universitas Islam Negeri Raden Fatah Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.19109/jusifo.v9i2.21357

Abstract

The public response in Pekanbaru to parking policies and regulations has given rise to various sentiments, both positive and negative. This discussion extends not only within the local community but also across various social media platforms. This research aims to analyze public sentiment towards the new parking policies and regulations in the Pekanbaru area. The study involves the KNN, NBC, and SVM algorithms to classify public sentiment into positive, neutral, and negative categories. Balancing techniques used in this research include Random Over Sampling (ROS) and Random Under Sampling (RUS). The data utilized in this study were obtained from posts on the social media platform X. The testing of the dataset using ROS resulted in high accuracy, precision, and recall values. The findings of this research indicate that overall, the SVM algorithm outperforms KNN and NBC in terms of accuracy, precision, and recall. Additionally, the most dominant sentiment is negative, with 422 tweets expressing dissatisfaction with the current parking policies.
A Comparative Study of the Performance of KNN, NBC, C4.5, and Random Forest Algorithms in Classifying Beneficiaries of the Kartu Indonesia Sehat Program Nabillah, Putri; Permana, Inggih; Afdal, M.; Muttakin, Fitriani; Marsal, Arif
JUSIFO : Jurnal Sistem Informasi Vol 10 No 1 (2024): JUSIFO (Jurnal Sistem Informasi) | June 2024
Publisher : Program Studi Sistem Informasi, Fakultas Sains dan Teknologi, Universitas Islam Negeri Raden Fatah Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.19109/jusifo.v10i1.21536

Abstract

This study evaluates the performance of various algorithms in determining eligible recipients for the Kartu Indonesia Sehat program. The Random Forest algorithm demonstrated the highest accuracy, precision, and recall, with values of 72.08%, 72.41%, and 99.64%, respectively. The emphasis on recall helps minimize errors in identifying eligible recipients. Additionally, the C4.5 algorithm reduced the total number of variables from 33 to 8, highlighting its computational efficiency. The findings provide valuable insights for the Social Affairs Office of Dumai City in making informed decisions regarding KIS eligibility. The results underscore the effectiveness of using algorithmic approaches to enhance the accuracy and efficiency of aid distribution processes.
Pengelolaan Limbah Rumah Tangga menjadi Eco-Enzyme melalui Proses Fermentasi Indriyani; Tafzi, Fitry; Afdal, M.; Ulyarti; Lisani
Studium: Jurnal Pengabdian Kepada Masyarakat Vol 3 No 3 (2024): Studium: Jurnal Pengabdian Kepada Masyarakat
Publisher : WIDA Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53867/jpm.v3i3.107

Abstract

Forum Indonesia Muda (FIM) is an independent forum consisting of young people from various backgrounds, universities, and youth movements from all over Indonesia, with the aim of building the nation through a spirit of collective contribution. With the diverse backgrounds of its members, each individual has their own role, leading FIM Jambi to have several divisions to channel the youth roles effectively. Organic waste is material that is discarded but still has potential for reuse, especially fruit and vegetable peels that can be processed into liquid and solid products beneficial to the environment. This liquid is known as eco-enzyme, which is highly beneficial for health, the environment, and agriculture. This activity aims to reduce the amount of fruit and vegetable peel waste. The community service activity is divided into four stages of implementation, namely: the survey stage, the socialization stage, the technical training stage, and the mentoring stage. The results of this activity are expected to generate a significant positive impact on waste management and environmental sustainability, particularly for FIM as youth who play a role in environmental preservation. This activity provides a solution that can be widely applied in the community to create a cleaner and healthier environment.
Efektivitas Model Pembelajaran Berbasis Proyek Terhadap Keaktifan dan Kemampuan Mahasiswa Suseno, Rahayu; Indriyani, Indriyani; Afdal, M.; Nizori, Addion
Jurnal Inovasi dan Teknologi Pembelajaran Vol 9, No 1 (2022)
Publisher : Universitas Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17977/um031v9i12022p090

Abstract

Abstrak: Penelitian ini bertujuan untuk mengetahui efektivitas pembelajaran berbasis proyek dalam meningkatkan aktivitas dan kemampuan mahasiswa. Metode penelitian yang digunakan adalah Weak Experimental dengan desain One Group Pretest – Posttest yang dilakukan kepada mahasiswasemester lima pada mata kuliah Sanitasi Industri, Program studi Teknologi Hasil Pertanian. Pengumpulan data menggunakan instrument tes dan angket. Analisis data dilakukan dengan analisis deskriptif. Hasil penelitian menunjukkan bahwa kemampuan mahasiswa meningkat berdasarkan n-gain rata-rata kelas yang masuk dalam kriteria sedang sebesar 0,39. Tanggapan mahasiswa terhadap pembelajaran berbasis proyek termasuk kedalam kriteria sangat tertarik (94,92%) dengan persentase sangat setuju 30,98% dan setuju 63,93%. Metode pembelajaran berbasis proyek efektif dalam meningkatkan aktivitas dan kemampuan mahasiswa pada mata kuliah Sanitasi Industri.Abstract: This study aims to determine the effectiveness of project-based learning in increasing student activities and abilities. The research method used is Weak Experimental with the design of One Group Pretest – Posttest which is carried out to fifth semester students in the Industrial Sanitation course, Agricultural Product Technology Study Program. Collecting data using test instruments and questionnaires. Data analysis was done by descriptive analysis. The results showed that the student's ability increased based on the n-gain average class that was included in the moderate criteria of 0.39. Student responses to project-based learning were included in the criteria for being very interested (94.92%) with the percentage strongly agreeing 30.98% and agreeing 63.93%. Project-based learning methods are effective in increasing student activities and abilities in Industrial Sanitation courses.
Comparison of Service and Ease of e-Commerce User Applications Using BERT Yuda, Afi Ghufran; Novita, Rice; Mustakim; Afdal, M.
Jurnal Sistem Cerdas Vol. 7 No. 2 (2024)
Publisher : APIC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37396/jsc.v7i2.403

Abstract

The development of e-commerce has transformed shopping patterns by harnessing the internet, enabling consumers to shop online. In Indonesia, e-commerce has experienced rapid growth, with numerous options such as Tokopedia, Shopee, and Lazada, leading to intense competition. Sentiment analysis using machine learning techniques has become crucial for understanding consumer views on these e-commerce services. This study analyzes user comments on Tokopedia, Shopee, and Lazada e-commerce platforms from Instagram social media, totaling 3900 data points, using the Bidirectional Encoder Representations from Transformers (BERT) model with 5 epochs and a batch size of 32. Sentiment analysis utilizes 3 types of labels: positive, neutral, and negative. The final results of the study include the performance analysis of the BERT model, as well as comparisons for each predefined category, namely Promotions & Offers, and Services. The final results of the model indicate good performance, with accuracy rates of 95%, 97%, and 99%, respectively.
Penerapan Algoritma K-Medoids dan FP-Growth dengan Model RFM untuk Kombinasi Produk Pertiwi, Tata Ayunita; Afdal, M.; Novita, Rice; Mustakim, Mustakim
Building of Informatics, Technology and Science (BITS) Vol 6 No 2 (2024): September 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i2.5268

Abstract

Competition in the business world has increased, resulting in companies having to optimize sales and retain their customers. Customers are an important company asset that must be well looked after. The aim of customer segmentation is to understand customer purchasing behavior so that companies can implement appropriate marketing strategies. Aurel Mini Mart is a retail business that does not yet consider the recency, frequency and monetary value of customer shopping. So far, promotions have been carried out only based on estimates, without taking into account accurate data and information. This research combines the RFM model with data mining techniques to segment customers. Based on the 5 clusters formed from the clustering process, gold customers are in cluster 1 which has high loyalty with low recency value, high frequency and high monetary value. This shows that customers in this segment often make purchases for quite large amounts of money. Meanwhile, customers in clusters 2, 3, 4, and 5 are dormant customers who rarely make transactions and the amount of money spent is also small. After the customer segmentation process is complete, the next step is to use the FP-Growth Algorithm to associate the products purchased by customers. This aims to obtain a better product combination, so that the sales strategy can be more effective and the company can make a profit.
Implementasi Algoritma Random Forest Untuk Analisa Sentimen Data Ulasan Aplikasi Pinjaman Online Digoogle Play Store Wibisono, Yudistira Arya; Afdal, M.; Mustakim, Mustakim; Novita, Rice
Building of Informatics, Technology and Science (BITS) Vol 6 No 2 (2024): September 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i2.5368

Abstract

Online lending programs are examples of financial service platforms offered directly by commercial fintech players. However, there are rampant cases of fraud and unethical actions by some online lenders such as threatening and harassing billing methods due to late payments. This research aims to classify sentiment from user reviews of online loan applications on the Google Play Store into positive, negative, or neutral categories. This research conducts sentiment analysis of user reviews of online loan applications such as AdaKami, AdaModal, Cairin, FinPlus and UangMe using a text mining approach. This approach can perform sentiment classification on user reviews quickly. Data was collected using the scrapping technique on the Google Play Store and obtained a total of 200 data on each online loan application. The modeling used in this research is the division of training data and test data as much as 80:20. The highest accuracy results using the Random Forest algorithm are Cairin and UangMe applications with 85% accuracy. While the application that gets the lowest accuracy result is the AdaModal application with 75% accuracy. A visualization analysis using word clouds was also conducted to understand the context of user reviews of the pinjol apps. The results show that users almost always discuss loan limits in every sentiment across the five apps.
Implementasi Algoritma Fuzzy C-Means menggunakan Model LRFM untuk Mendukung Strategi Pengelolaan Pelanggan Aini, Delvi Nur; Afdal, M.; Novita, Rice; Mustakim, Mustakim
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 3 (2024): Juli 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i3.7616

Abstract

The same treatment of all customers will cause customers who are not so valuable to become value destroyers in the concept of Customer Relationship Management. Providing discounts and promos to all customers without differentiating customer segments has not provided significant benefits for a company. These two things are being experienced by BC 4 HNI Pekanbaru, so changes are needed in evaluating the strategies taken to maintain relationships with customers and form segments according to customer characteristics. Customer segments can be analyzed from sales transaction data. The purpose of this study is to manage and group sales transaction data in determining customer segmentation so that the strategy is more targeted. The analysis of customer transaction data was carried out by grouping the data using the Fuzzy C-means algorithm and the length, recency, frequency, monetary (LRFM) model, and AHP weighting.  The formation of the number of validated clusters of the silhouette index and ranking is carried out by multiplying the weight of AHP to find the customer lifetime value (CLV) so that it can be known which customer groups provide high value to the company. The result of this study is that BC 4 HNI Pekanbaru customers are grouped into 2 segments, namely the potential customer group which has a fairly frequent transaction value with an average monetary value of Rp. 2,802,495.00 and a fairly high number of transactions contribute greatly to the Company and the new customer group which means a new customer segment with uncertain funds, an average monetary of Rp. 104,567.00. Based on the segment, BC 4 HNI Pekanbaru can carry out a strategy in managing its customers according to the type of segment generated from this research.
Co-Authors - Mardalena, - AA Sudharmawan, AA Addion Nizori Aini, Delvi Nur Andaranti, Arifah Fadhila Arif Marsal Darlis Darlis Eki Saputra Febi Nur Salisah, Febi Nur FITRY TAFZI Indriyani Indriyani Indriyani Inggih Permana Intan, Sofia Fulvi Jazman, Muhammad Kusuma, Gathot Hanyokro Lisani Mawaddah, Zuriatul Megawati - Mustakim Mustakim Mustakim Mutia, Risma Muttakin, Fitriani Nabillah, Putri Pertiwi, Tata Ayunita Priady, Muhamad Ilham Putra, Moh Azlan Shah Rice Novita Saitul Fakhri Suhessy Syarif, Suhessy Suryadi Suryadi Suryani, Heni Suseno, Rahayu Teja Kaswari Wibisono, Yudistira Arya Winnugroho Wiratman, Manfaluthy Hakim, Tiara Aninditha, Aru W. Sudoyo, Joedo Prihartono Yuda, Afi Ghufran Yurleni Yurleni Zarnelly Zarnelly