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SAFITRI JUANITA
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Idealis : Indonesia Journal Information System
Published by Universitas Budi Luhur Jakarta
ISSN : -     EISSN : 26847280     DOI : -
Core Subject : Science,
Computer Science & IT Decision Sciences, Operations Research & Management
Jurnal Indonesia Journal Information System (Idealis) adalah jurnal penelitian Program Studi Informasi, Fakultas Teknologi Informasi, Universitas Budi Luhur. Topik pada Jurnal ini adalah Decision Support System, E-Commerce/E-Business, Datawarehouse/BI, Enterprise System, Data Mining, Sistem Penunjang Keputusan selamat membaca,  Admin Jurnal Idealis
Arjuna Subject : Ilmu Komputer (semua kategori) - Sistem Informasi
Articles 901 Documents
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ANALISIS SENTIMEN PADA ULASAN APLIKASI EHADRAH DI GOOGLE PLAYSTORE MENGGUNAKAN SUPPORT VECTOR MACHINE (SVM) Muzayyanah, Ayu Basirotul; Pawening, Ratri Enggar; Arifin, Zainal
IDEALIS : InDonEsiA journaL Information System Vol. 7 No. 2 (2024): Jurnal IDEALIS Juli 2024
Publisher : Universitas Budi Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36080/idealis.v7i2.3250

Abstract

Aplikasi Ehadrah adalah sebuah aplikasi untuk mendengarkan dan mengakses konten hadrah, seni musik tradisional islami. Jumlah pengguna aplikasi Ehadrah semakin meningkat di era digital saat ini karena popularitasnya yang terus bertambah. Namun, ulasan pengguna sering tidak sesuai dengan rating yang ditampilkan di Google Play Store, menciptakan kesenjangan yang dapat menghambat pengembangan aplikasi lebih lanjut. Oleh karena itu, diperlukan analisis sentimen untuk memahami sentimen pengguna secara lebih mendalam. Data ulasan diperoleh melalui web scraping menggunakan Google Play Store API dan kemudian diproses melalui tahapan preprocessing seperti case folding, tokenizing, stopword removal, dan stemming. Pemodelan menggunakan teknik Support Vector Machine (SVM) dengan membandingkan kedua kernel SVM yaitu Linear dan RBF untuk mengklasifikasikan sentimen dalam ulasan pengguna. Tujuan penelitian ini adalah untuk mengevaluasi sentimen pengguna terhadap aplikasi eHadrah di Google Play Store dengan menggunakan algoritma SVM. Hasil penelitian menunjukkan bahwa SVM dengan kernel Linear lebih unggul dibandingkan kernel RBF. SVM dengan Kernel Linear menghasilkan akurasi 95.46%, precision 81.83%, recall 55.61%, dan f-measure 62.82%, sementara kernel RBF menghasilkan akurasi 94.15%, precision 58.03%, recall 40.33%, dan f-measure 43.24% dengan menggunakan 976 data ulasan.
ANALISIS SENTIMEN PADA MEDIA SOSIAL TERHADAP LAYANAN SAMSAT DIGITAL NASIONAL DENGAN SUPPORT VECTOR MACHINE Kirana, Anindya Sasi; Rusdah, Rusdah; Roeswidiah, Ririt; Pudoli, Ahmad
IDEALIS : InDonEsiA journaL Information System Vol. 8 No. 1 (2025): Jurnal IDEALIS Januari 2025
Publisher : Universitas Budi Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36080/idealis.v8i1.3276

Abstract

Motor vehicle users experience rapid growth every year. The increasing number of vehicles contributes to one of the state revenues: taxes. SAMSAT is a state institution with the authority to regulate motor vehicle tax (PKB). As technology develops, SAMSAT innovates through the SIGNAL application, which allows people to make motor vehicle tax payments safely via cell phone. Social media such as Instagram and X have great potential for collecting data to understand public reactions to the SIGNAL application. Comments on social media regarding the SIGNAL application raise pros and cons from the public; therefore, it is necessary to carry out sentiment analysis through a text mining approach using the Support Vector Machine (SVM) algorithm following the Cross Industry Standard Process for Data Mining (CRISP-DM) methodology. This research was carried out through several stages: data collection, preprocessing, modeling with the Support Vector Machine (SVM), and evaluation with a confusion matrix. Data in the research were collected from Instagram social media comments from September 20, 2023, until. 16 April 2024 as many as 3,543 records and 1,335 comments on X's social media from 31 May 2023 until March 27, 2024, with the keyword "SIGNAL application". After the preprocessing stage, the data used was reduced to 3,911 because there were duplicate and irrelevant reviews. based on 3,911 data, it produced 773 positive comments, 1991 negative, and 1147 neutral comments. This research aims to identify public sentiment towards SIGNAL services via social media, such as Instagram. We prepared a dataset of two and three sentiment classes for research modeling needs. Based on the application of the model, a Support Vector Machine (SVM) with a linear kernel produces better scores than the Naïve Bayes and KNN models with accuracy values ​​of 0.88, precision of 0.88, recall of 0.81, and AUC of 0.92 using a 10-fold cross-validation on training data and test data.
ANALISIS FORENSIK CITRA DI PLATFORM X MENGGUNAKAN METODE DIGITAL FORENSIC RESEARCH WORKSHOP (DFRWS) Sulistyo, Wicaksono Yuli; Pratiwi, Septia Ayu; Hidayatullah, Muhammad Haedar Zhafran
IDEALIS : InDonEsiA journaL Information System Vol. 8 No. 1 (2025): Jurnal IDEALIS Januari 2025
Publisher : Universitas Budi Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36080/idealis.v8i1.3293

Abstract

One of the negative impacts observed is the uncontrolled behavior of individuals in using personal applications, which has the potential to lead to cybercrimes. Common cases of cybercrime on social media involve posting photos for bullying purposes, spreading false information, fraud, and defamation. Photos can be easily altered or edited, enabling the manipulation of information that could be exploited for malicious activities. Nowadays, the ease of uploading photos and the difficulty in detecting their authenticity are key factors underlying this study, which aims to detect digital photo forgery in the platform X. The study adopts the Digital Forensic Research Workshop (DFRWS) framework to identify instances of digital photo forgery. The key stages implemented in this research include identification, preservation, collection, examination, analysis, and presentation. The findings revealed pixel alterations in the smartphone object area and other regions, indicating manipulation and making the image appear different from the original. Additionally, using FotoForensic, metadata from the photos was successfully retrieved, revealing that the second photo had been edited and previously modified using Adobe Photoshop CC 2017. Furthermore, analysis with ForensicallyBeta identified pixel discrepancies in several parts of the image, showing additional noise, which confirmed that the photo had been edited in specific areas, thus indicating its lack of authenticity.
IMPLEMENTASI METODE AGILE PADA RANCANG BANGUN SISTEM INFORMASI CAREER DEVELOPMENT CENTER PERGURUAN TINGGI Dwi Ardiada, I Made; Prawira Kusuma, Agus Tommy Adi; Sarwa Edy, Raden Agus; Ade Saputra, I Gede Pramana
IDEALIS : InDonEsiA journaL Information System Vol. 8 No. 1 (2025): Jurnal IDEALIS Januari 2025
Publisher : Universitas Budi Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36080/idealis.v8i1.3300

Abstract

Higher education institutions in the digital era are faced the challenge of providing effective and efficient career services for their students. The current problem is the process of providing information on job vacancies in universities which is still posted in paper form and placed on glass walls. Apart from that, there are still many universities that do not have online information services to support careers for graduating students, one of which is the Career Center Service. On the partner side (agencies/industry) which require human resources, they still conventionally provide career information to universities in paper form which requires a glass wall to display the information so that it can be read by visitors who need the information. This research aims to design a university Career Development Center (CDC) Information System by implementing the Agile Method to overcome this problem. With the Agile method, the CDC information system design process in university becomes more effective and efficient. The stages of the Agile method are planning, Design, Testing, implementation, Review and Launch. The results obtained in this research are the CDC information system with 3 level access rights, namely student affairs, partners (company/industry agencies), and alumni/visitors. This research aims to create a CDC information system that can help universities have online services related to the Development of students who have graduated in obtaining job vacancy information flexibly, besides that students who have graduated and partners (agencies/industry) can online access information/provide vacancy information without the need to submit vacancy information papers to universities.
PENGEMBANGAN SISTEM PENDUKUNG KEPUTUSAN BERBASIS MOBILE DALAM PEMILIHAN KUALITAS TELUR PADA KURNIAJAYA FARM Saputra, Ricco Herdiyan; Waziana, Winia; Sari, Dita Novita; Pratomo, Panji Andhika
IDEALIS : InDonEsiA journaL Information System Vol. 8 No. 1 (2025): Jurnal IDEALIS Januari 2025
Publisher : Universitas Budi Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36080/idealis.v8i1.3301

Abstract

The demand for high-quality chicken eggs continues to increase, requiring an accurate and efficient assessment system to ensure the quality of products sold by egg agents. Kurniajaya Farm faces challenges in selecting the quality of chicken eggs that vary, so a technology is needed that can assist in objective decision making. This study aims to develop an egg quality assessment model using the simple additive weighting (SAW) method integrated into the mobile application development life cycle to facilitate the selection of quality chicken eggs. The application of the SAW method is due to its ability to simplify the assessment process by calculating the weight of various predetermined criteria. The criteria set in this study include egg size, egg weight, shell cleanliness, and shell thickness. The purpose of the study is to create a system that can help Kurniajaya Farm determine egg quality quickly, accurately and consistently through an easily accessible mobile application. The research findings indicate that the system development has succeeded in categorizing egg quality with high accuracy. Users can easily determine which eggs meet the desired quality standards through the mobile application. The implementation of this system at Kurniajaya Farm has been shown to improve the quality of the products produced and customer satisfaction.
SISTEM INFORMASI KEHADIRAN SOPIR PADA PT BLUE BIRD TBK BERBASIS WEB Hayati, Putri; Harsanto, Kukuh; Fauzie, Achmad Rizki Nur; Rifai, Anwar
IDEALIS : InDonEsiA journaL Information System Vol. 8 No. 1 (2025): Jurnal IDEALIS Januari 2025
Publisher : Universitas Budi Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36080/idealis.v8i1.3341

Abstract

The transportation industry heavily depends on effective human resource management. PT Blue Bird Tbk, a leading transportation company in Indonesia, requires an information management system to efficiently oversee its human resources, particularly the operational team. This study aims to create a website driver attendance monitoring framework for implementation at PT Blue Bird Tbk. The goal is to enhance operational team management efficiency by providing a web-based application that assists in work scheduling and attendance tracking. Besides offering flexibility in data management, the system enables access to real-time information. Using the waterfall model for software development, the research encompasses the stages of requirements analysis, system design, implementation, testing, and maintenance. During the analysis phase, Data was gathered by means of firsthand observation and interviews to understand existing business processes. The application was developed with ReactJS and Go for the user interface and system logic, while MySQL was utilized for database management. The black box technique test findings demonstrate that all features in the web-based driver attendance monitoring information system function properly. This research resulted when creating and designing a web-based system that allows real-time and efficient monitoring of driver attendance. Thus, the management of PT Blue Bird Tbk can monitor driver attendance more effectively.
IMPLEMENTASI FRAMEWORK SCRUM DALAM PENGEMBANGAN DASHBOARD MONITORING UNTUK OPTIMASI PENGELOLAAN DATA INTERFACE Wazaumi, Dwi Diana; Saputro, Vian Ardiyansyah; Nisa, Seftia Khairun; Zahrani, Sharla Adhita
IDEALIS : InDonEsiA journaL Information System Vol. 8 No. 1 (2025): Jurnal IDEALIS Januari 2025
Publisher : Universitas Budi Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36080/idealis.v8i2.3338

Abstract

In today's digital era, data that is managed efficiently and effectively is a success for a company when facing problems in an ever-changing business. PT XYZ is one of the many companies in the automotive sector, PT XYZ needs to ensure that their interface data can be managed optimally. However, interface data management is often faced with various challenges, such as large volumes of data, continuous monitoring, and the need to identify problems that will arise early. In order to overcome these problems, PT XYZ has a plan to design an application called MyZ as a monitoring solution through a dashboard that aims to monitor files and find potential problems related to interface data. The focus of this study is on the development of the MyZ application as a monitoring dashboard and adopting the Scrum approach to optimize data interface management at PT XYZ. The findings obtained from the development of the MyZ application, this application can show the time needed to send data via email an average of 3.3 seconds, this indicates that the MyZ application allows users to receive information quickly, so that it can speed up the decision-making process. While the presentation of information in the dashboard takes an average of 1.5 seconds. This shows that the MyZ application can provide a fast and efficient response in supporting the interface data monitoring process and minimizing waiting time. This efficiency is very relevant in a dynamic work environment and requires a fast response to data changes.
A DECISION SUPPORT SYSTEM FOR THE DETERMINATION OF ADDITIONAL STOCK ITEMS USING THE TOPSIS METHOD BASED ON ANDROID Santoso, Firdaus Restu Rafi; Ujianto, Erik Iman Heri
IDEALIS : InDonEsiA journaL Information System Vol. 8 No. 1 (2025): Jurnal IDEALIS Januari 2025
Publisher : Universitas Budi Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36080/idealis.v8i2.3339

Abstract

Inefficient stock management can lead to problems such as overstocking or stockouts, incorrect pricing, and difficulties in identifying best-selling products, which negatively affect the performance and profitability of Toko Twins Pancing Temanggung while reducing customer satisfaction. To overcome these issues, this study develops a mobile-based decision support system (DSS) using the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method, designed to assist store owners in determining stock additions, managing inventory, and identifying best-selling products. The steps taken include problem identification, data collection, system architecture design, TOPSIS implementation, and system testing. TOPSIS was selected for its ability to provide accurate recommendations by considering various relevant criteria. Decision-making is influenced by factors such as price, stock quantity, and weekly sales. The study results indicate that this system can effectively recommend stock additions by prioritizing products with sufficient stock and good sales performance. For example, the product with the highest preference value is the Red Angle fishing rod (0.8963), which is prioritized for restocking. The system, tested with data from Toko Twins Pancing Temanggung, achieved a 98% accuracy rate compared to manual calculations. Users can conveniently access this system via mobile devices, enabling decision-making anytime and anywhere. This DSS enhances operational efficiency and business performance at Toko Twins Pancing Temanggung, providing a significant solution for stock management and offering a more structured and efficient approach to achieving higher profits.
ANALISIS PERBANDINGAN K-NEAREST NEIGHBORS DAN NAIVE BAYES UNTUK REKOMENDASI PILIHAN PROGRAM STUDI BAGI MAHASISWA Nurwati, Nurwati; Santoso, Yudi
IDEALIS : InDonEsiA journaL Information System Vol. 8 No. 1 (2025): Jurnal IDEALIS Januari 2025
Publisher : Universitas Budi Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36080/idealis.v8i1.3344

Abstract

Kesulitan memilih program studi bagi lulusan Sekolah Menengah Atas (SMA) dan Sekolah Menengah Kejuruan (SMK) merupakan tantangan besar bagi alumni. Tantangan ketidakpastian karir di masa depan yang sering kali menjadikan proses pemilihan program studi semakin rumit. Tantangan lainnya, banyak alumni siswa SMA belum sepenuhnya memahami minat bakat yang dimiliki. Berbeda dengan lulusan SMK yang telah mendapatkan pendidikan dan pengalaman kerja magang selama di sekolah. Tekanan orang tua, faktor biaya, keterbatasan informasi yang dimiliki alumni mengenai program studi yang diimpikan, serta memilih antara minat dan peluang karir merupakan faktor yang dipertimbangkan bersama orang tua. Dengan demikian, permasalahan penelitian ini adalah tantangan apa saja yang dihadapi oleh lulusan SMA juga SMK dalam memperoleh informasi yang memadai mengenai program studi di perguruan tinggi impian serta bagaimana lulusan mengatasi kebingungan dalam menentukan program studi yang cocok dengan bakat, minat serta prospek kerjanya. Untuk mengatasi hal ini tujuan penelitian memberikan rekomendasi untuk calon mahasiswa dalam menentukan program studi yang cocok berdasarkan latar belakang akademik dan kemampuan calon mahasiwa yang dimiliki. Rekomendasi program studi ini menggunakan metode text mining dengan membandingkan hasil nilai akurasi antara algoritma K-Nearest Neighbors (KNN) dengan algoritma Naive Bayes. Perbandingan algoritma ini mendukung pengambilan keputusan. Data set yang digunakan data mahasiswa tiga angkatan total 347 data. Selanjutnya, data dibagi menjadi data latih dan data uji. Akurasi metode KNN tercatat sebesar 81,16% dengan nilai K=2 dan proporsi data uji sebesar 40%. Akurasi Naive Bayes mencapai 82,61% program studi Teknik Informatika. Hasil akurasi tidak menunjukkan perbedaan yang signifikan, namun metode Naive Bayes menghasilkan akurasi yang lebih tinggi dibandingkan KNN.
IMPLEMENTASI MACHINE LEARNING DALAM PENGELOMPOKAN MUSIK MENGGUNAKAN ALGORITMA K-MEANS CLUSTERING Hakim, Bhustomy; Kaunang, Fergie Joanda; Susanto, Cornelius; Salim, Jonathan; Indradjaja, Reynaldi
IDEALIS : InDonEsiA journaL Information System Vol. 8 No. 1 (2025): Jurnal IDEALIS Januari 2025
Publisher : Universitas Budi Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36080/idealis.v8i1.3357

Abstract

Music is an inseparable part of everyone's life. Many people listen to music but with different preferences because there are so many different types of music available. Many music streaming platforms compete to make song recommendations that suit their users' preferences but it is still difficult to group the music in them. This study aims to analyze music using the K-Means Clustering algorithm, an unsupervised machine learning method, to group songs based on their features such as tempo, tone, and other elements. This research was conducted in the context of the rapidly growing digitalization of music, where music streaming platforms are increasingly popular and allow for personalization of user preferences. The K-Means algorithm is used to find patterns from various music genres, so that it can provide insight into music trends and listener preferences. This study involves several main stages, including data exploration (Exploratory Data Analysis/EDA), checking for missing values ​​and outliers, and selecting relevant features. Furthermore, the clustering process is carried out using the K-Means algorithm with evaluation through the Elbow and Silhouette methods to determine the optimal number of clusters and assess the quality of clustering. This research is expected to contribute to the development of a better music recommendation system by increasing knowledge in the field of machine learning, especially in the application of the K-Means algorithm for music data clustering.

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