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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 Sistem informasi dan informatika (SIMIKA) 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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Analisis Sentimen Terhadap Publisher Rights Dalam Mengunggah Konten Digital Menggunakan Ensemble Learning Putri, Anisa; Mustakim, Mustakim; Novita, Rice; Afdal, M
Building of Informatics, Technology and Science (BITS) Vol 6 No 1 (2024): June 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i1.5179

Abstract

Digital content encompasses various forms of information, ranging from informative text to interactive videos. YouTube, as one of the most popular social media platforms, is widely used in Indonesia. However, the proposed Publisher Rights Bill or the Draft Presidential Regulation on the Responsibility of Digital Platforms for Quality Journalism has sparked debate. In the context of YouTube, this regulation has the potential to threaten content creators. Negative reactions from various parties highlight concerns about the impact of this regulation. Therefore, this study aims to analyze sentiment towards Publisher Rights in the uploading of digital content using an ensemble learning approach. The analysis found that 60% of the sentiment was negative, reflecting concerns about copyright, royalties, or ethical issues. A total of 32% of the sentiment was neutral, indicating uncertainty or a lack of information, and only 8% of the sentiment was positive, supporting the policy of protecting publisher rights and recognizing their value and contributions. This study employed ensemble techniques based on Bagging (Random Forest) and Boosting (Adaboost), where the accuracy of Random Forest was higher at 83% compared to Adaboost's accuracy of 68%.
Perbandingan Algoritma Linear Regression, Support Vector Regression, dan Artificial Neural Network untuk Prediksi Data Obat Putri, Suci Maharani; Novita, Rice; Mustakim, Mustakim; Afdal, M
Building of Informatics, Technology and Science (BITS) Vol 6 No 1 (2024): June 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i1.5184

Abstract

Regression is a crucial focus in various fields aiming to forecast future values to aid decision-making and strategic planning. Different regression algorithms have their advantages and disadvantages, and their performance can vary depending on the data characteristics. Therefore, further analysis is needed to identify the appropriate algorithm that provides the best solution for the problem at hand. This study compares three popular regression algorithms: Linear Regression (LR), Support Vector Regression (SVR), and Artificial Neural Network (ANN) to predict drug data at a pharmacy in Riau province. Currently, the pharmacy lacks an accurate method for estimating monthly drug needs, relying instead on rough estimates. This often results in either shortages or overstock, leading to losses, especially if the drugs expire. Three types of drugs, namely Amoxicillin, Antacids, and Paracetamol were selected to test the proposed algorithms. The analysis and comparison show that the SVR algorithm outperforms the others on all three drug types when focusing on the RMSE metric. However, when the focus is on the MAPE metric, the ANN algorithm proves to be superior. Although LR does not excel in any metric, all three algorithms (LR, SVR, and ANN) have MAPE values below 10%, indicating highly accurate predictions. This accuracy is evidenced by the prediction results of all proposed models, which effectively follow the patterns and trends in the actual data
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.
Sentimen Analisis Social CRM Pada Media Sosial Instagram Menggunakan Machine Learning Untuk Mengukur Retensi Pelanggan F. Safiesza, Qhairani Frilla; 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.v6i2.5269

Abstract

To create and maintain a superior competitive advantage in a knowledge-based economy, businesses must be able to utilize data and manage customer relationships through the implementation of Customer Relationship Management (CRM), particularly Social CRM. Social CRM is a renewal of business strategy that is created to engage customers in a collaborative conversation and create mutually beneficial value in a trusted and transparent business environment. Seeing this development as one of the successful culinary companies in the Souvenir sector in Pekanbaru, the company must be able to process all the information obtained. Currently, the company has never analyzed comments on social media, especially the Instagram account. These comments are useful for evaluation material and can be a parameter of customer satisfaction and to see the potential for customer retention. To assess positive and negative comments on the Instagram account, sentiment analysis can be carried out using machine learning, namely 3 classification algorithms, namely Naive Bayes Classifier (NBC), Support Vector Machine (SVM) and Random Forest (RF). The sentiment results show that the SVM and NBC algorithms obtain the best accuracy of 74.26% compared to RF, and the results of the social CRM analysis show that customers are more satisfied with the company in terms of products, services, and actions taken by the company, so that the company is considered capable of retaining its customers.
Analisis Sentimen Masyarakat Terhadap Pinjaman Online di Twitter Menggunakan Algoritma Naïve Bayes Classifier dan K-Nearest Neighbor Afandi, Rival; 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.v6i2.5300

Abstract

The very rapid development of technology has had a big impact on humans. The influence of technological developments that we can feel is in the financial sector. One thing that is quite popular lately is online loans. Pinjol or online loan is a fast and easy online money lending service via an application or website, with fast approval and disbursement, but often has high interest and short tenors. On Twitter, review comments and information used are stored in text form. One of the processes for retrieving text mining information in the text category is Sentiment Analysis to see whether a sentiment or opinion tends to be Positive, Negative or Neutral in the reviews of Pinjol application user comments. In the data collection results there were 600 initial data, namely 122 Positive reviews, 432 Negative reviews and 43 Neutral reviews. Then the sentiment classification process using the Naive Bayes and K-NN algorithms produces accuracy, precision and recall of 68%; 83% and recall 74% on the Naive Bayes algorithm, while the results of accuracy, precision and recall on K-NN are 72%; 74% and recall 96% with experiments using 80% training data and 20% test data
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.
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.
Analisis Sentimen Masyarakat Mengenai Gerakan Childfree di Media Sosial X Menggunakan Algoritma NBC dan SVM: Sentiment Analysis of Childfree Campaign on X Social Media Using NBC and SVM Algorithms Putra, Moh Azlan Shah; Permana, Inggih; Afdal, M.
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 4 No. 4 (2024): MALCOM October 2024
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v4i4.1356

Abstract

Anak merupakan salah satu entitas yang umum dalam membentuk sebuah keluarga, namun dalam beberapa tahun kebelakang muncul pembahasan mengenai childfree. Dengan banyaknya perdebatan pro-kontra mengenai childfree, perlu dilakukannya sentimen analisis terkait isu ini. Penelitian ini bertujuan untuk menganalisis sentimen masyarakat mengenai gerakan childfree di media sosial X menggunakan algoritma Naïve Bayes Classifier (NBC) dan Support Vector Machine (SVM). Sentimen dibagi menjadi 3 kelas yaitu positif, negatif, dan netral. Penelitian ini mengumpulkan data dengan crawling data pada media sosial X dengan keyword childfree. Data yang diperoleh merupakan data teks mentah sehingga dibutuhkan tahap pra proses. Tahap pra proses yang dilakukan adalah tokenizing, case folding, filter stopword, stemming, TF-IDF, dan data balancing. Berdasarkan simulasi, performa algoritma NBC adalah: akurasi = 56,36%, presisi = 56,41%, dan recall = 56,35%, sedangkan performa algoritma SVM adalah: akurasi 76,12%, presisi 76,36%, dan recall 76,13%. Sehingga dapat disimpulkan bahwa SVM memiliki performa yang lebih baik dari pada NBC pada analisis sentimen di penelitian ini.
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 Andaranti, Arifah Fadhila 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 Indah Lestari, Indah 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 Syaifullah Syaifullah T. T. Poy Teja Kaswari Tri Astuti Triningsih, Elsa Tshamaroh, Muthia Ula, Walid Alma Wibisono, Yudistira Arya Wilrose, Anandeanivha Winnugroho Wiratman, Manfaluthy Hakim, Tiara Aninditha, Aru W. Sudoyo, Joedo Prihartono Y Zaharanova Yuda, Afi Ghufran Yulianti, Nelvi Yun Alwi Yurleni Yurleni Yusuf Amirullah Luber Zarnelly Zarnelly Zarqani, Zarqani