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All Journal JUSIFO : Jurnal Sistem Informasi Sistemasi: Jurnal Sistem Informasi JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Jurnal CoreIT JURNAL MEDIA INFORMATIKA BUDIDARMA INOVTEK Polbeng - Seri Informatika Jurnal Sistem Cerdas Building of Informatics, Technology and Science Malcom: Indonesian Journal of Machine Learning and Computer Science The Indonesian Journal of Computer Science
Rice Novita
Universitas Islam Negeri Sultan Syarif Kasim Ria
Automotive Engineering Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering

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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.
Implementasi The Concurrent Development Model Untuk Membangun Learning Management System Novita, Rice; Munzir, Medyantiwi Rahmawita; Kurniawan, Viki
Jurnal Inovtek Polbeng Seri Informatika Vol 9, No 1 (2024)
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/isi.v9i1.3879

Abstract

Technology plays an important role in the educational process. The weakness of the current educational process is that there is no media that helps store data, share data and monitor data properly. Learning Management System (LMS) is a web-based software program that has five main elements for management, documentation, monitoring, reporting, administration and distribution of educational content. This research aims to develop an LMS that is in accordance with the five main features in the LMS. with software development methods using The Concurrent Development Model. In this model, work activities are carried out simultaneously, each work process has several work triggers for the activity. Triggers can come from the beginning of the work process or from other triggers because each trigger will be interconnected. In system design, the concept of Object-Oriented Analysis Design (OOAD) is used with use case diagrams, activity diagrams and class diagrams.
Systematic Literature Review: Perbandingan Algoritma Klasifikasi Pangestu, Prayugo; Novita, Rice; Mustakim, Mustakim
Jurnal Inovtek Polbeng Seri Informatika Vol 8, No 2 (2023)
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/isi.v8i2.3698

Abstract

Seiring waktu, banyak metode data mining yang dibuat dan disarankan untuk membantu dalam mengambil keputusan. Karna keterbatasan dalam sumberdaya atikel ini hanya memberi kajian literatur secara sistematis dalam membandingkan performa metode Naïve Bayes, Decision Tree, Neural Network, Random Forest, Support Vector Machine untuk mengetahui metode mana yang paling efektif digunakan dalam mengklasifikasi dan memprediksi. Setelah dilakukan studi literatur dengan mengambil artikel dari rentang waktu 2019 sampai 2023 didapatkan sebanyak 500 artikel yang mengunakan metode Naïve Bayes, Decision Tree, Neural Network,  Random Forest, Support Vector Machine. Karna artikel yang didapat dalam pencarian awal begitu banyak, dibuatlah kriteria ingklusi dan eksklusi untuk memilah artikel yang memang sesuai dengan penelitian ini, setelah melakukan proses kriteria ingklusi dan eksklusi, didapatkan sebanyak 243 artikel dan diketahui bahwa topik yang lebih banyak dibahas adalah prediksi, yaitu berjumlah 122 artikel dan sisanya 121 artikel membahas tentang klasifikasi. Pada bidang prediksi metode yang paling sering digunakan adalah Random Forest degan jumlah 45 artikel dan rata rata tingkat akurasinya 91,18%, sedangkan pada bidang klasifikasi metode yang paling sering digunakan adalah Support Vector Machine dengan total 33 artikel dan rata rata akurasinya 88,85% 
SISTEM INFORMASI MONITORING INSYIRA PEKANBARU BERBASIS WEB MENGGUNAKAN AGILE DEVELOPMENT Novansyah, Rizky; Novita, Rice; Munzir, Medyantiwi Rahmawati; Nursalisah, Febi
Jurnal Inovtek Polbeng Seri Informatika Vol 9, No 1 (2024)
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/isi.v9i1.3813

Abstract

Perkembangan teknologi informasi saat ini memiliki dampak signifikan pada berbagai aspek kehidupan di Indonesia. Sistem Informasi Manajemen (SIM) menjadi elemen kunci bagi pelaku bisnis, termasuk Rumah Produksi Insyira di Pekanbaru, untuk meningkatkan efisiensi dan produktivitas. Meski demikian, rumah produksi ini menghadapi tantangan dalam mengelola bahan baku dan proses produksi, sehingga memerlukan solusi yang efektif. Penelitian ini bertujuan untuk merancang dan menerapkan "Sistem Informasi Monitoring Satu Pintu" di Rumah Produksi Insyira, berdasarkan metode Agile Development dan pendekatan Object Oriented Analysis Design (OOAD). Fokusnya adalah mengoptimalkan pengumpulan dan pemrosesan data produksi, mempermudah pemantauan stok bahan baku, serta mendukung pengambilan keputusan manajemen. Dengan adopsi sistem ini, diharapkan efisiensi dan efektivitas produksi Rumah Produksi Insyira dapat meningkat. Keamanan data menjadi perhatian utama dalam pengembangan sistem ini. Implementasi ini diharapkan dapat meningkatkan kualitas produk, memperbaiki efisiensi waktu produksi, dan memberikan dukungan yang lebih baik dalam pengambilan keputusan manajemen. Secara keseluruhan, diharapkan "Sistem Informasi Monitoring Satu Pintu" menjadi solusi terintegrasi yang mendukung pertumbuhan dan keberlanjutan bisnis Rumah Produksi Insyira. Sistem ini telah didukung dan diuji menggunakan metode pengujian blackbox dan User Acceptance Testing (UAT), dengan hasil nilai 96%, menunjukkan penerimaan yang baik dari pengguna.
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 Abror, Naufal Afandi, Rival Afdal, M.Afdal Afifah, Tiara Afrah Ahsyar, Tengku Khairil Aini, Delvi Nur Akhiril Anwar Harahap Al-Qadr, Nola Ardelia Andriawan, Ahmad Rizky Anisa Putri Auliani, Sephia Nazwa Azzahra, Aura Dwinnie, Zairy Cindy F. Safiesza, Qhairani Frilla Hamzah, Muhammad Luthfi Khairil Ahsyar, Tengku Kurniawan, Viki M Afdal M. Afdal Muhammad Iqbal Munzir, Medyantiwi Rahmawati Munzir, Medyantiwi Rahmawita Mustakim Mustakim Mustakim Mustofa, Asdar Novansyah, Rizky nursalisah, febi Pangestu, Prayugo Pertiwi, Tata Ayunita Pulungan, Joliando Putri, Suci Maharani Rahayu, Dwi Sri Ramadani, Faradila Rozanda, Nesdi Evrilayan Sasmita, Indah Vamilina, Vina Wibisono, Yudistira Arya Yuda, Afi Ghufran Zarnelly Zarnelly Zarnelly