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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
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Analisis Sentimen Traveloka Berdasarkan Ulasan Google Play Store Menggunakan Algoritma Support Vector Machine dan Random Forest Rohimah, Siti; Afdal, M; Mustakim, Mustakim; Novita, Rice
Building of Informatics, Technology and Science (BITS) Vol 6 No 3 (2024): December 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i3.6300

Abstract

The internet has become a key element in supporting technological and information advances in various sectors of human activity. In the trade and tourism sector, the Traveloka application is the favorite choice of Indonesian people. Reviews or reviews from users play an important role for the Company to understand the level of customer satisfaction. However, currently there are several users who give high ratings but contain negative reviews. Based on these problems, this research aims to understand more deeply user opinions, so that they can be used to improve services and features as well as test and compare the accuracy of the two algorithms in classifying user sentiment. In this research, the Support Vector Machine and Random Forest classification methods were used. The research results show that Random Forest has superior and stable performance compared to SVM, with higher average accuracy for most features, such as Traveloka (71% & 67%) and Airplanes (75% & 74%). Evaluation with k-fold cross validation supports these results, with higher average Random Forest accuracy on features such as Traveloka (70% & 66%) and Airplanes (75% & 74%).
Implementasi Metode Holt-Winters dan FP-Growth dalam Melakukan Peramalan Stok Barang Pada Swalayan Berdasarkan Pola Asosiasi Loka, Septi Kenia Pita; Afdal, M; Novita, Rice; Mustakim, Mustakim
Building of Informatics, Technology and Science (BITS) Vol 6 No 3 (2024): December 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i3.6305

Abstract

At present, competition in the business world is extremely fierce, particularly in the convenience store sector. The development of retail trade is progressing rapidly, accompanied by the emergence of many small markets and online shops. This situation encourages store owners to make wiser decisions, such as managing stock replenishment. If overlooked, this matter way hinder employees from locating the necessary items, thereby increasing the potential risk of goods expiring or being damage before they are sold. Therefore, store owners need to understand consumer behavior and shopping habits to assist iin stock management. Based on this issue, the research aims to analyze consumer purchasing patterns and optimize inventory stock. The result of this experiment identified two best rules, namely biscuit and consumption/food, with a confidence of 53,61%, a support of 15,57%, and a lift ratio of 1,116 the error measurement MAPE shows a value of 6,79 using alpha, beta and gamma values of 0,1. The total predicted stock in 52,086 with an actual value of 72,275, which is close to actual value of data prior to the significant observed in the last three months.
Analisa Manejemen Risiko Sistem Informasi Penjualan Menggunakan Metode Failure Mode Effects and Analysis (FMEA) Syahri, Alfi; -, Megawati; Afdal, M; Nur Salisah, Febi
JATISI Vol 11 No 4 (2024): JATISI (Jurnal Teknik Informatika dan Sistem Informasi)
Publisher : Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/jatisi.v11i4.8438

Abstract

Teknologi informasi telah banyak diterapkan di berbagai perusahaan, salah satunya PT XYZ yang telah menerapkan sistem informasi penjualan atau BEI sebagai otomatisasi proses bisnis perusahaan. Namun pada praktiknya manajemen risiko sistem informasi BEI belum diterapkan dengan baik, masih terdapat kendala dan insiden pengelolaan. Penelitian ini bertujuan untuk menganalisis manajemen risiko sistem informasi ISX dengan menggunakan framework FMEA (Failure Mode Effects and Analysis). FMEA dapat mengidentifikasi risiko berdasarkan tingkat keparahan suatu kejadian, seberapa sering kejadian tersebut terjadi, dan seberapa tinggi tingkat deteksinya. Dari variabel-variabel tersebut akan dihasilkan Risk Priority Number (RPN) yang dapat menentukan prioritas risiko atau permasalahan yang paling kritis. Hasil penelitian ini menunjukkan bahwa sistem ISX mempunyai 2 risiko dengan tingkat sangat tinggi, 1 risiko dengan tingkat tinggi, 1 risiko dengan tingkat menengah, 21 risiko dengan tingkat rendah, dan 5 risiko dengan tingkat sangat rendah. Risiko yang berada pada tingkat yang sangat tinggi dan tinggi merupakan risiko yang diprioritaskan dan perlu mendapatkan tindakan dan pengendalian risiko.
Prediksi Produksi Kelapa Sawit Menggunakan Algoritma Support Vector Regression dan Recurrent Neural Network Alfakhri, Rezky; Permana, Inggih; Novita, Rice; Afdal, M
Building of Informatics, Technology and Science (BITS) Vol 6 No 3 (2024): December 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i3.6441

Abstract

Oil palm is one of the important plantation crops and a leading commodity in Indonesia. PT. XYZ is a company engaged in receiving Fresh Fruit Bunches (FFB) to be processed into Crude Palm Oil (CPO) and Palm Kernel (PK). So far, the company has conducted statistical analysis with a correction value of 5% - 12% on the production results each month in targeting production results. However, this method is still lacking, because it uses manual calculations and considers estimates from personal experience. Therefore, this research proposes a data mining technique with Support Vector Regression (SVR) and Recurrent Neural Network (RNN) algorithms to predict production output precisely. In this study, testing was carried out on SVR hyperparameters, namely Kernel, C, Gamma, and Epsilon. While in RNN, testing is carried out on the optimizer, and the learning rate. In addition, the window size is also determined through a series of experiments, namely 3, 5, and 7. The comparison results show that the RNN model outperforms SVR with an RMSE value of 0.0928, MAPE of 14.32%, and R2 of 0.6164. The RNN model was then implemented to predict the next 3-month period. The prediction results show that there will be a significant increase in production in the first month, then a slight decrease in the second month, and an increase again in the third month.
Segmentasi Pelanggan Menggunakan Fuzzy C-Means dan FP-Growth Berdasarkan Model LRFM untuk Rekomendasi Produk Rahmah, Astriana; Afdal, M
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.v8i3.7737

Abstract

Bazmart Pelalawan is a part of the National Zakat Agency (BAZNAS) program in Pelalawan Regency, which has implemented strategies to retain customers. However, these strategies have not yet succeeded in fully understanding customer characteristics, resulting in a decline in customer trust and their willingness to shop again. Additionally, Bazmart lacks proper guidelines for offering products that meet customer needs. This research aims to enhance product recommendations by integrating LRFM analysis into data mining techniques. The parameters considered include customer LRFM values, customer segmentation, and products frequently purchased together over a year of transaction data. Fuzzy C-Means and FP-Growth algorithms were used for segmentation and association analysis. The segmentation results identified two customer clusters with a Davies-Bouldin Index (DBI) value of 0.628, indicating good cluster quality. In the association analysis, a minimum support (minsup) of 30% and a minimum confidence (mincof) of 70% were used, resulting in 8 rules for cluster 1 and 17 rules for cluster 2. From the two association pattern results, the highest rules were obtained, namely in Drinks and Snacks and Bread with a support value of 0.426 and a confidence value of 0.926 resulting in a value of 0.394. These rules provide insights that Bazmart Pelalawan can use to develop more effective and targeted direct marketing strategies for each customer cluster. Thus, this research is expected to help Bazmart Pelalawan better understand customer characteristics and improve customer loyalty through more targeted product recommendations.
Penerapan Algoritma Fuzzy C-Means untuk Klasterisasi Customer Lifetime Value menggunakan Model LRFMD Ramadhani, Indah; Afdal, M; Mustakim, Mustakim; Zarnelly, Zarnelly
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.v8i3.7613

Abstract

PT X is a retail company engaged in printing. The company has not differentiated between information about profitable and unprofitable customers for the company. Transaction data is only used as profit and loss information so they do not know the characteristics of the customers they have. In addition, the lack of extensive services in the merchandise category is one of the reasons the company's revenue has not reached the predetermined target. Currently, the company has opened additional services in the merchandise field. This research aims to identify customer segmentation as well as analyze the characteristics and provide a strategy proposal that will be submitted to PT. X. Customer loyalty and characteristics have a significant impact on a company. To identify customers who show loyalty to the company, the Fuzzy C-Means algorithm is used to perform clustering, using the Davies Bouldin Index (DBI) to evaluate the clustering results. The model used is in accordance with the principles of Length, Recency, Frequency, Monetary and Diversity (LRFMD) to categorize purchasing patterns. By analyzing LRFMD variables, it is possible to identify customers who are loyal to the company and those who are not. This research produces 6 clusters with the best cluster or supestar customer in cluster 6, the second best value customer or golden customer is cluster 2, the average value customer or typical customer is cluster 4 and 5 and the lowest cluster or dormant customer is in cluster 3.
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.
Klasifikasi Sentimen Pengguna X Terhadap Pemboikotan Produk Pro Israel Menggunakan Algoritma Machine Learning Susanti, Pingki Muliya; Afdal, M; Permana, Inggih; Marsal, Arif
Building of Informatics, Technology and Science (BITS) Vol 6 No 4 (2025): March 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i4.6533

Abstract

The campaign to boycott pro-Israel goods emerged as a result of the enduring conflict between Israel and Palestine. This boycott initiative led to a decline in sales, which adversely impacted the livelihoods of employees, manifesting in diminished bonuses, salary reductions, and job terminations. Such actions elicited a variety of reactions from the public on platform X. This study seeks to categorize the sentiments of X users regarding the boycott of pro-Israel products by comparing the efficacy of Machine Learning algorithms, namely Support Vector Machine and Random Forest. To address the class imbalance within the dataset, this research employs the synthetic minority over-sampling technique (SMOTE). The dataset comprised 2,275 entries, gathered through web scraping methods on the X platform. The findings indicate that a majority of X users in Indonesia endorse the boycott movement, exhibiting a positive sentiment of 58%. The SVM algorithm, when combined with SMOTE, demonstrated the highest performance in sentiment classification, achieving an accuracy of 90.54%, whereas Random Forest attained an accuracy of only 83.1%. This research offers insights into the views of the Indonesian populace regarding the boycott of pro-Israel products.
Analisis Sentimen Terhadap Program Makan Bergizi Gratis Menggunakan Algoritma Machine Learning Pada Sosial Media X Triningsih, Elsa; Afdal, M; Permana, Inggih; Rozanda, Nesdi Evrilyan
Building of Informatics, Technology and Science (BITS) Vol 6 No 4 (2025): March 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i4.6534

Abstract

The government has launched the Free Nutritious Meal Program as part of a strategic effort to reduce stunting in Indonesia. However, the program has generated a lot of controversy among the public, especially regarding the large budget allocation that is considered burdensome and its impact on the education sector and the country's financial stability. This study aims to analyze public sentiment towards the program by utilizing data from social media platform X (Twitter) as much as 2,400 data. Public sentiment is classified into three categories, namely positive, negative, and neutral, using two machine learning algorithms, namely Support Vector Machine (SVM) and Random Forest. In addition, the SMOTE technique is used to handle data imbalance in the model training process. The analysis results showed that negative sentiments dominated at 46%, with the main issue highlighted being the high budget allocation and its impact on education. In terms of performance, the SVM algorithm with SMOTE produced the highest accuracy of 85.74%, outperforming the Random Forest algorithm which only achieved 81.53% accuracy.
FERMENTASI JERAMI JAGUNG MENGGUNAKAN KAPANG TRICHODERMA HARZIANUM DITINJAU DARI KARAKTERISTIK DEGRADASI Suryadi, Suryadi; Darlis, Darlis; Syarif, Suhessy; Afdal, M.
Jurnal Karya Abdi Masyarakat Vol. 1 No. 1 (2017): Jurnal Karya Abdi Masyarakat
Publisher : LPPM Universitas Jambi

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (103.982 KB) | DOI: 10.22437/jkam.v1i1.3727

Abstract

Penelitian ini bertujuan untuk mengetahui lama waktu fermentasi dan karakteristik degradasi komponen serat jerami jagung fermentasi secara In sacco. Fermentasi jerami jagung secara padat menggunakan Trichoderma harzianum sebagai stater. Sebanyak 2,5 gram urea, 2,5 gram molases, 2,5 ml sediaan Trichoderma harzianum dicampur dengan air menjadi 20 ml yang kemudian disemprotkan pada 1000 gram jerami jagung segar. Selanjutnya jerami jagung dimasukkan ke dalam toples plastik dan diperam sesuai dengan perlakuan yaitu 4, 8, 12 dan 16 hari. Uji karakteristik degradasi jerami jagung fermentasi dilakukan dengan metode In sacco atau nylon bag technique. Sebanyak 6 gram sampel jerami dimasukkan ke dalam kantong nylon dengan ukuran 140, 80 mm diinkubasi ke dalam rumen sapi dengan interval waktu 6, 12, 24, 48 dan 72. Penelitian ini menggunakan rancangan acak lengkap (RAL) dengan perlakuan 4 lama fermentasi dan ulangan 3 untuk tiap perlakuan. Peubah yang diukur adalah karakteristik degradasi meliputi : Nilai fraksi a, nilai fraksi b dan nilai fraksi c dari NDF, ADF dan Hemiselulosa Jerami jagung fermentasi. Hasil penelitian ini menunjukkan bahwa lama fermentasi berpengaruh nyata terhadap nilai fraksi (a) dan nilai fraksi (b) dari NDF, nilai fraksi (c) dari ADF dan Hemiselulosa, tetapi tidak berpengaruh nyata pada fraksi (a) dan fraksi (b) dari ADF, fraksi (a) dan fraksi (b) dari hemiselulosa, fraksi (c) dari NDF jerami jagung fermentasi. Kesimpulan: Fermentasi jerami jagung dengan Trichoderma harzianum dapat meningkatkan nilai fraksi (a) dari NDF, ADF dan laju degradasi NDF ADF dan Hemiselulosa. Lama fermentasi yang terbaik pada jerami jagung fermentasi diperoleh pada perlakuan 16 hari.
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