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All Journal Foristek JUSIFO : Jurnal Sistem Informasi JOURNAL OF APPLIED INFORMATICS AND COMPUTING CCIT (Creative Communication and Innovative Technology) Journal Jurnal Teknik Informatika (JUTIF) The Indonesian Journal of Computer Science
Rinianty, Rinianty
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

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SISTEM PENDUKUNG KEPUTUSAN PENERIMAAN KARYAWAN MENGGUNAKAN METODE SAW PADA CV. GREEN ADVERTISING Rinianty Rinianty; Sukardi Sukardi
CCIT Journal Vol 11 No 1 (2018): CCIT JOURNAL
Publisher : Universitas Raharja

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (466.305 KB) | DOI: 10.33050/ccit.v11i1.558

Abstract

Green Advertising is one of the businesses engaged in typing and printing services. In supporting the future of the company needed quality human resources. The selection process of employee acceptance on Green Advertising is still done manually, sorting the file of applicants and then compare with the predefined criteria. This takes a long time to affect the efficiency of decision making. From these problems, then needed a sistem that can help the leadership of Green Advertising in the receipt of employees. In this research used decision support sistem using Simple Additive Weighting method with prototype development model. The Simple Additive Weighting method is a weighted sum method used to find the optimal alternative of a number of alternatives with certain criteria. The criteria used in the research of the employee selection decision support sistem are the last educational criteria, work experience, skill and completeness of the file. Result of research from Decision Support Support Sistem Employee use computerized sistem and manual sistemsprovide the same alternative options.
WEBSITE PUSAT MANAJEMEN RISIKO DI LEMBAGA PENJAMINAN MUTU PENGEMBANG PEMBELAJAR rini, RINIANTY; Nisa, Chairunisa; Fitri, Siti Fitriana
Foristek Vol. 14 No. 2 (2024): Foristek
Publisher : Foristek

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54757/fs.v14i2.586

Abstract

Pada era digital seperti saat ini, website telah menjadi salah satu sarana yang paling efektif dalam berkomunikasi dengan khalayak luas. Hal ini juga berlaku untuk Lembaga Pengembangan Manajemen dan Manajemen Risiko Perguruan Tinggi di Lembaga Penjaminan Mutu dan Pengembang Pembelajar yang membutuhkan platform online untuk menyebarkan informasi dan layanan terkait manajemen risiko. Dalam upaya membangun sebuah website pusat manajemen risiko LPMPP, penting untuk memilih platform yang dapat menyediakan kemudahan dalam pengelolaan konten serta memiliki tampilan yang menarik. Salah satu platform yang direkomendasikan adalah WordPress, yang merupakan salah satu platform website paling populer dan mudah digunakan saat ini. Pusat manajemen risiko dapat mengikuti best practice dalam pembuatan aplikasi sistem manajemen risiko, seperti menggunakan standar NIST SP 800-30 Revision 1. Best practice ini membantu dalam mengelola risiko dengan lebih baik dan mengurangi kerugian yang mungkin terjadi. Dengan demikian, website Pusat Manajemen Risiko berbasis WordPress dirancang untuk memberikan layanan dan informasi yang efektif dan efisien kepada pengunjung.
Design and Implementation of a Customer Relationship Management System for Medium-Sized Digital Printing Enterprises Wongkar, Noel Marcell Jonathan; Wirdayanti, Wirdayanti; Syahrullah, Syahrullah; Rinianty, Rinianty; Lapatta, Nouval Trezandy
JUSIFO : Jurnal Sistem Informasi Vol 10 No 2 (2024): JUSIFO (Jurnal Sistem Informasi) | December 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.v10i2.25023

Abstract

This study investigates the design and implementation of a Customer Relationship Management (CRM) system specifically developed to address the operational challenges faced by medium-sized enterprises in the digital printing sector, with Rio Digital Printing as a case study. The research identifies key issues such as communication gaps and the lack of real-time order tracking, which negatively impact customer satisfaction. Employing a prototyping methodology, the system was iteratively refined with active user participation, ensuring alignment with stakeholder requirements. Key features include real-time order tracking, automated notifications, and a comprehensive interactive dashboard to support data-driven decision-making. The results demonstrate that the CRM system significantly enhances operational transparency, improves customer engagement, and fosters loyalty. This study contributes to the academic discourse by addressing the underexplored application of CRM systems in small and medium-sized enterprises, presenting a scalable framework for adaptation in similar industries. The findings also provide practical implications, advocating for digital transformation as a strategy to improve competitiveness in dynamic market environments.
Application of Webqual 4.0 and Importance Performance Analysis Methods in Analyzing The Quality of Information Technology SIPENDEKAR Services Kartika, Rina; Syahrullah, Syahrullah; Rasmita, Hajra; Rinianty, Rinianty; Yanti, Wirda; Lapatta, Nouval Trezandy
CCIT (Creative Communication and Innovative Technology) Journal Vol 18 No 1 (2025): CCIT JOURNAL
Publisher : Universitas Raharja

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/ccit.v18i1.3569

Abstract

Information technology plays a very important role, especially in the field of education, one of which is through the use of websites. The Department of Information Engineering at Tadulako University is one of the universities that utilizes websites, one of which is the information system for administrative services and final assignments (SIPENDEKAR). Sipendekara is designed to facilitate the submission of titles, guidance, and proposal exams, final exams, and thesis exams. But so far, there has been no research that measures the quality of the sipendekar website based on perceptions of usability, information quality, and service interaction quality using the IPA method. This study aims to determine the quality of Sipendekar services using the webqual 4.0 method as the basis for determining the questionnaire. Then the Importance Performance Analysis method to analyze questionnaire data to identify attributes that have met and have not met user expectations. The results showed an average level of conformity of 92.63%. The average gap analysis result is -0.33. The results of the Cartesian quadrant there are 4 attributes in the first quadrant that are prioritized for improvement and there are 9 attributes that achieve a high level of satisfaction and only need to maintain quality.
PUBLIC SENTIMENT ANALYSIS OF 'DIRTY VOTE' DOCUMENTARY FILM ON TWITTER USING NAÏVE BAYES WITH GRID SEARCH OPTIMIZATION Bagaskara, Febrian Chrissma; Syahrullah, Syahrullah; Hendra, Andi; Lamasitudju, Chairunnisa; Rinianty, Rinianty
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 6 (2024): JUTIF Volume 5, Number 6, Desember 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.6.2682

Abstract

The film "Dirty Vote" provides a realistic depiction of alleged fraud issues within Indonesia's democratic system, released ahead of the 2024 elections. This has sparked various public opinions, both in favor of and against the film, potentially affecting the stability of Indonesia’s democratic system. The aim of this research is to analyze the public's reaction to the "Dirty Vote" documentary, which could serve as a consideration for assessing public awareness in rationally responding to a film and improving the quality of democracy in Indonesia. This research will test the accuracy of data used in classification using the Naive Bayes Classifier based on collected Twitter data. The evaluation results of the Naive Bayes model for sentiment classification showed an accuracy of 86%, with a precision of 84% and a recall of 91%. When compared to the implementation of hyperparameter tuning using grid search with a stratified k-fold combination and parameter configurations for alpha: [0,1], binarize: [0.0], and fit prior: [true, false], better results were obtained with an accuracy of 90%, a precision of 87%, and a recall of 94%. This demonstrates that using parameter optimization methods from grid search can help improve the accuracy of a classification model. It is hoped that this research will contribute significantly to the development of Indonesia’s democratic system, particularly in raising public awareness to think more rationally and critically when evaluating and analyzing a film.
a PREDIKSI SISWA PUTUS SEKOLAH DAN KEBERHASILAN AKADEMIK MENGGUNAKAN MACHINE LEARNING: Prediksi Siswa Putus Sekolah dan Keberhasilan Akademik Fitriana, Siti; riniyanty; laila, rahma; pratama, septiano anggun; lamasitudju, chairunnisa ar
The Indonesian Journal of Computer Science Vol. 13 No. 6 (2024): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i6.4453

Abstract

In 2020-2023. The issue of high school dropouts at SMA Negeri 2 Sigi increased, causing impacts such as declining school accreditation, a decrease in the number of students, and operational aid. This research aims to build an early prediction system for student dropouts using Machine Learning (ML). In this study, data from 200 students were used. With 16 students labeled as dropout. The results showed a model accuracy of 0.942 and an area under the curve (AUC) of 0.948. the factors most influencing student droppout are average grades, meeting targets, and father’s education leve.
APPLICATION OF VGG16 ARCHITECTURE IN WOOD TYPE CLASSIFICATION USING CONVOLUTIONAL NEURAL NETWORK Afiah, Nurul Anggun; Syahrullah, Syahrullah; Ardiansyah, Rizka; Laila, Rahmah; Pohontu, Rinianty
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 1 (2025): JUTIF Volume 6, Number 1, February 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.1.3874

Abstract

Wood is an important natural resource in construction and the furniture industry, with various types possessing unique characteristics. The selection of wood types is often done manually, which is prone to errors that can negatively impact the working process, product quality, and the sustainability of the forests that source the wood. Therefore, this research aims to improve classification accuracy through the application of technology. This study utilizes Convolutional Neural Network (CNN) with the VGG16 architecture to process images in analyzing the visual characteristics of wood, with the goal of building a model capable of classifying wood types based on images. The dataset used consists of 1,584 samples of wood images sourced from Kaggle. Four models were tested with variations in the training and validation data splits, as well as the use of Adam and Adamax optimizers, over 100 epochs. Model 1 achieved a training accuracy of 96.68% and a testing accuracy of 98.10%. Model 2, with a training accuracy of 99.47% and a testing accuracy of 98.41%, showed the best performance. Models 3 and 4 also yielded testing accuracies of 97.46% and 97.78%, respectively. The results of this study indicate that the application of CNN with the VGG16 architecture can enhance the effectiveness of wood type classification and contribute to more accurate and efficient wood selection practices.
Optimization of Inventory Management with QR Code Integration and Sequential Search Algorithm: A Case Study in a Regional Revenue Office Fajar, Moh; Azhar, Ryfial; Anshori, Yusuf; Laila, Rahma; ., Rinianty; Lapatta, Nouval Trezandy
Journal of Applied Informatics and Computing Vol. 9 No. 2 (2025): April 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i2.8919

Abstract

Inventory management at a government office was previously conducted manually, leading to issues such as data inaccuracies, delays in item searches, and low work efficiency. This study develops a web-based inventory management system integrated with QR Code technology and a sequential search algorithm to address these challenges. The system was developed using the prototyping method, with iterative design based on user feedback until the final version met the office's operational needs. Key features of the system include digital inventory recording, item tracking using QR Codes, and real-time information access through a web-based interface. The system was tested in two stages: simulation and direct implementation in a real-world environment, involving 10 respondents to evaluate effectiveness and usability. The test results showed a 95% improvement in data recording accuracy, a 60% reduction in item search time, and an average user satisfaction score of 77.25 based on the System Usability Scale (SUS). This research successfully improved inventory management efficiency and demonstrated the system’s potential for adoption by other similar organizations, with modular adjustments tailored to their needs.
Sentiment Analysis for the 2024 DKI Jakarta Gubernatorial Election Using a Support Vector Machine Approach Mariani, Mariani; Angreni, Dwi Shinta; Nur, Sri Khaerawati; Rinianty, Rinianty; Jayanto, Deni Luvi
Journal of Applied Informatics and Computing Vol. 9 No. 2 (2025): April 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i2.9260

Abstract

This study analyzes public sentiment regarding candidates in the 2024 DKI Jakarta Gubernatorial Election utilizing a Support Vector Machine (SVM) approach. Recognizing the pivotal role of social media, particularly Twitter, in shaping public opinion, the research addresses the challenges of processing large volumes of unstructured data. Through systematic data preprocessing and feature extraction, the SVM model was applied, achieving a sentiment classification accuracy of 70%. The analysis revealed a distribution of sentiments where 36.1% of comments were positive, 33.4% negative, and 30.5% neutral. These findings illustrate the complexities of public discourse surrounding key political events, highlighting the model's efficacy and the nuances of sentiment detection. Moreover, discussions on model limitations elucidate areas for enhancement, suggesting future avenues including the adoption of more sophisticated algorithms and improved data processing techniques. This research contributes to the understanding of voter sentiment dynamics in a significant electoral context, providing insights that may assist campaign strategies and political analyses in Indonesia.
Implementation of Collaborative Filtering in the Salted Fish Recommendation Process Rizky, Moh Taufiq; Rinianty, Rinianty; Nugraha, Deny Wiria; Amriana, Amriana; Lapatta, Nouval Trezandy
Journal of Applied Informatics and Computing Vol. 9 No. 6 (2025): December 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i6.11576

Abstract

The development of e-commerce in the current era has been so rapid that buying and selling transactions are carried out online through various media, including websites and applications. With so many products available in the application, users often feel confused when choosing the product they want to buy, so it takes a long time to choose a product to avoid regret after purchasing it. In this study, a web-based recommendation system was created for the process of recommending salted fish with the aim of making it easier for customers to choose the type of salted fish. The Collaborative Filtering method was used, employing Pearson Correlation as a tool to calculate the similarity value between users, then using Weighted Sum to calculate the prediction value. Collaborative Filtering often experiences the cold start problem, where the system has difficulty providing recommendations to users who do not yet have a transaction history. Therefore, the author proposes a popularity-based strategy as a measure to overcome this problem. Based on testing, the author obtained results of MAE = 0.63 and RMSE = 0.81 based on train-test split results with a data distribution of 80:20, 80% of the dataset for training and 20% of the dataset for testing with an accuracy of 70-80%, indicating that this system works well. This system has been tested using the Blackbox method.
Co-Authors Afiah, Nurul Anggun Amriana Amriana Andi Hendra Bagaskara, Febrian Chrissma Chairunnisa Ar. Lamasitudju Deni Luvi Jayanto Deny Wiria Nugraha Dwi Shinta Angreni Fajar, Moh Fitri, Siti Fitriana Kartika, Rina Laila, Rahma Lamasitudju, Chairunnisa Mariani Mariani Nisa, Chairunisa Nouval Trezandy Lapatta Pratama, Septiano Anggun Rahmah Laila Rasmita, Hajra Rizka Ardiansyah Rizky, Moh Taufiq Ryfial Azhar, Ryfial Siti Fitriana Sri Khaerawati Nur Sukardi Sukardi Syahrullah Syahrullah Wirdayanti Wongkar, Noel Marcell Jonathan Yanti, Wirda Yusuf Anshori