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Format : Jurnal Ilmiah Teknik Informatika, Fakultas Ilmu Komputer Universitas Mercu Buana, Jl. Raya Meruya Selatan, Kembangan, Jakarta 11650 Tlp./Fax: +62215840816
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INDONESIA
Format : Jurnal Imiah Teknik Informatika
Published by Universitas Mercu Buana
ISSN : 20895615     EISSN : 27227162     DOI : http://doi.org/10.22441/format
Core Subject : Science,
Computer Science & IT
Format : Jurnal Ilmiah Teknik Informatika merupakan jurnal peer-review yang berasal dari hasil-hasil penelitian dan kajian ilmiah di bidang Ilmu Komputer khususnya Informatika. Cakupan naskah artikel yang dapat dipublikasikan difokukuskan pada bidang berikut (namun tidak terbatas): ICT, Rekayasa Perangkat Lunak, Sistem Informasi Geografis, Data mining and Big Data, Komunikasi Data, Mobile Computing, Kesercasan Buatan, E-Learning, Multimedia and Pengolahan Gambar, Sistem Keamanan dan Basisdata, IOT, dan Jaringan Komputer. Format : Jurnal Ilmiah Teknik Informatika diterbitkan oleh Program Studi Informatika, Universitas Mercu Buana Jakarta. Periode penerbitan adalah setahun dua kali, yaitu pada bulan Januari dan bulan Juli.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 140 Documents
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PERANCANGAN SISTEM INFORMASI PEMESANAN MAKANAN BERBASIS ANDROID UNTUK MENINGKATKAN PENJUALAN BAGI UMKM Yahya, Yahya; Sidik, Sidik
FORMAT Vol 13, No 2 (2024)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/format.2024.v13.i2.004

Abstract

Tantangan yang dihadapi oleh Usaha Mikro, Kecil, dan Menengah (UMKM) dalam industri makanan untuk meningkatkan penjualan dan menjangkau pasar yang lebih luas di era digital. Persaingan yang ketat dan perubahan perilaku konsumen yang lebih memilih layanan daring menjadi pendorong utama bagi UMKM untuk mengadopsi teknologi informasi dalam operasional bisnis mereka.Penelitian ini menggunakan metode penelitian pengembangan sistem dengan pendekatan Waterfall yang meliputi tahap analisis kebutuhan, desain sistem, implementasi, pengujian, dan pemeliharaan. Metode ini dipilih karena memiliki struktur yang terorganisir dan memudahkan dalam pengelolaan setiap tahap pengembangan.Aplikasi yang akan dibuat adalah sistem informasi pemesanan makanan berbasis Android. Aplikasi ini dirancang untuk membantu UMKM dalam mengelola menu, menerima pesanan, dan berinteraksi dengan pelanggan secara lebih efektif..Hasil penelitian menunjukkan bahwa aplikasi pemesanan makanan berbasis Android ini dapat meningkatkan efisiensi operasional dan penjualan bagi UMKM. Penggunaan aplikasi ini memungkinkan UMKM untuk menjangkau lebih banyak pelanggan, meningkatkan kepuasan pelanggan melalui kemudahan pemesanan, dan mengelola pesanan dengan lebih baik. Pengujian fungsionalitas dan integrasi menunjukkan bahwa aplikasi berjalan dengan baik dan memenuhi kebutuhan pengguna sesuai dengan spesifikasi yang telah ditentukan.Dengan demikian, penelitian ini menyimpulkan bahwa adopsi teknologi informasi dalam bentuk aplikasi pemesanan makanan berbasis Android adalah solusi efektif bagi UMKM untuk meningkatkan daya saing dan penjualan di pasar yang semakin digital.
Komparasi Algoritma Topic Modelling LDA VS LSA Pada Berita Detikcom Al Izzi, Ahmad Kemal; Pratama, Rakadian Audiga
FORMAT Vol 13, No 1 (2024)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/format.2024.v13.i1.005

Abstract

This research focuses on the process of applying Topic Modeling by comparing the Latent Semantic Analysis (LSA) and Latent Dirichlet Allocation (LDA) models on news tweet data taken from the Detikcom account. The process begins by crawling data over a one year period, starting from December 9, 2022 to December 9, 2023, resulting in 958 rows of data. Data pre-processing includes steps such as case folding, tokenization, stopwords removal, and stemming. After pre-processing, a bag of words process is carried out to calculate the frequency of word occurrences in each document. The number of word occurrence frequencies is used as a reference in creating LSA and LDA models. Each model has 8 topics, 10 iterations, and 42 random states. Topic production is carried out based on keywords that appear in the modeling results. Evaluation of the two models is carried out by measuring topic coherence or topic coherence using the c_v value. The LSA model shows a coherence value of 0.5, while the LDA model has a coherence value of 0.45. The evaluation results show that in this case, the LSA model has better performance than the LDA model based on the topic coherence value. As a suggestion for further research, researchers are expected to consider the use of other cases for topic modeling and other exploration models in Topic Modeling such as OCTIS. This can expand understanding of the performance of the Topic Modeling algorithm on X news data.
Applications for Learning Fruits And Animals Using Android-Based Augmented Reality Technology Lay, Yoseph Martin; Kaburuan, Emil Robert
FORMAT Vol 13, No 2 (2024)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/format.2024.v13.i2.009

Abstract

The use of Augmented Reality (AR) technology in education has become an area of research that has attracted attention in recent years. In the context of learning animals and fruits, AR has great potential to increase students' interest and understanding through interesting interactive experiences. By using Unity 3D as a development platform and Vuforia as an AR toolkit, this application is equipped with features such as object detection, visualization of fruits and animals in 3D, as well as additional information about names, descriptions, and additional information about characteristics and characteristics of each object. This study aims to develop a fruits and animals learning application using Android-based Augmented Reality (AR) technology. This application takes advantage of AR capabilities on Android devices to visualize fruits and animals in three dimensions (3D) in the real world.
WEB-BASED APPLICATION DESIGN FOR ADOPTION OF ABANDONED PETS Nauli, Sukarno Bahat; Kusumawati, Kiki; Sitorus, Hernalom; Chafid, Nurul; Panjaitan, Bosar; Rahmatina, Izazih
FORMAT Vol 13, No 1 (2024)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/format.2024.v13.i1.010

Abstract

Abstract :This research is action research or also known as action research. Pejaten shelter is an organization engaged in the rescue of abandoned animals in the special area of the capital city of Jakarta and its surroundings. Abandoned animals accommodated in this place are dogs and cats. The number of adopted animals available reaches 1700-2000 animals. So far, the adoption process is still relatively difficult, namely by going through the stages directly and disseminating information only through the Instagram platform. then the shelter pejaten still uses routine reports on animals that have been adopted manually. Therefore the purpose of this research is to develop a web-based application that helps animal adopters to find animals for adoption, as well as helping adoption animal providers to disseminate animal adoption information, take care of online adoption needs, and facilitate the administration of animal adoption administration. The research method is divided into data collection methods, design methods, development methods, and evaluation methods. Data collection methods are in the form of surveys, interviews, observations, literature studies, and analysis of similar applications. The design method is divided into User Interface design methods and system design methods. The development method applied is the waterfall method which is divided into Requirement analysis, design, implementation, Testing, Maintenance. The results of the research are in the form of a web-based application that makes it easier for adopters and providers of adopted animals to find and disseminate information on animal adoption effectively, as well as to facilitate the management of online adoption and administration of animals.Keywords : application, adoption animal, design, waterfall
Pengaruh Aplikasi CapCut: Menyelami Kreativitas dengan Ragam Template Video yang Memukau pada Generasi Z Sekaringtyas, Fia Wahyu; Djuniadi, Djuniadi; Hastawan, Ahmad Fashiha
FORMAT Vol 13, No 1 (2024)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/format.2024.v13.i1.002

Abstract

Technology, especially video editing apps like CapCut, has become an integral part of Generation Z's life in the digital age. CapCut provides intuitive features and a variety of templates to create engaging video content without the need for complex editing skills. In interviews with Generation Z students, CapCut's key features such as crop, speed, audio, text, transitions, filters, overlays, and templates were found to be attractive and supportive in the creative process. Nonetheless, there is a need to continue developing the app by adding more template options and providing greater flexibility for users in customizing content. Thus, CapCut can continue to be a relevant and effective tool for Generation Z in expressing themselves in the ever-evolving digital era.
Implementasi Algoritma Greedy dan Dynamic Programming untuk Masalah Penjadwalan Interval dengan Model Knapsack Prasha, Achmad Ardani; Rachmadi, Clavino Ourizqi; Sari, Amanda Puspita; Raditya, Nanda Garin; Mutiara, Sabrina Laila; Yusuf, Mohamad
FORMAT Vol 13, No 2 (2024)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/format.2024.v13.i2.005

Abstract

Penelitian ini membahas implementasi algoritma Greedy dan Dynamic Programming untuk penjadwalan interval dengan model knapsack, yang esensial dalam optimasi. Tujuan penelitian ini adalah memberikan panduan praktis dalam memilih algoritma yang tepat untuk aplikasi dunia nyata. Metode yang digunakan mencakup algoritma Greedy, yang membuat pilihan lokal terbaik untuk mencapai solusi global optimal, dan Dynamic Programming, yang memecah masalah menjadi submasalah lebih kecil dan menyelesaikannya secara berulang. Hasil penelitian menunjukkan bahwa Dynamic Programming memberikan solusi optimal dengan penggunaan waktu dan ruang yang lebih besar dibandingkan dengan Greedy. Algoritma Greedy lebih cepat tetapi tidak selalu memberikan solusi optimal, sedangkan Dynamic Programming lebih cocok untuk masalah kecil yang membutuhkan solusi optimal. Penelitian ini menyimpulkan bahwa kedua algoritma memiliki kelebihan dan kekurangan masing-masing tergantung pada skala dan kebutuhan masalah. Penelitian ini berkontribusi dalam bidang optimasi dan penjadwalan serta membuka jalan bagi pengembangan algoritma lebih lanjut. Implementasi kedua algoritma ini membantu dalam pengambilan keputusan yang lebih baik dalam aplikasi penjadwalan interval dengan model knapsack.Kata kunci: Algoritma Greedy, Dynamic Programming, Knapsack Problems, Interval Scheduling, Optimasi, Task Scheduling
Klasifikasi Kematangan Buah Pepaya Berdasarkan Fitur Warna Menggunakan Metode SVM Hafiizah, Nur; Saputra, Rizal Adi
FORMAT Vol 13, No 1 (2024)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/format.2024.v13.i1.006

Abstract

This research aims to develop a method for identifying the ripeness level of papaya based on color features using the Support Vector Machine (SVM) algorithm. In the introduction, it is emphasized that generally, the color changes in papaya skin serve as the primary indicator of ripeness, but the accuracy of human observations in distinguishing colors can sometimes be suboptimal. Therefore, this study focuses on utilizing the SVM algorithm, particularly recognized for its excellent classification capabilities, especially in image processing and classification.The initial step in the research method involves a literature review to gather the latest information on fruit ripeness classification, with a specific emphasis on color features. The subsequent steps include formulating problems and hypotheses to determine whether color-based classification methods, particularly SVM, can effectively classify papaya ripeness levels. The design and implementation phase encompass capturing papaya images using a smartphone camera, converting the images from RGB to LAB, and extracting color features using a multi-level SVM. Testing and evaluation are then conducted to assess the system's accuracy.The implementation results indicate an accuracy rate of 96%, categorizing papayas into three classifications: mature, partially mature, and immature. Evaluation metrics such as precision, recall, and F1-score provide in-depth insights into the system's performance, demonstrating SVM's capability in identifying papaya ripeness levels. In conclusion, this research successfully applies SVM as an effective method for classifying papaya ripeness based on color features, contributing to the development of an accurate and reliable automated system for fruit ripeness identification.
ANALISIS SENTIMEN TERHADAP DAMPAK PERANG ISRAEL - PALESTINA MELALUI DATA TWITTER MENGGUNAKAN NAIVE BAYES Halim, Alfian Noer; Dwiasnati, Saruni
FORMAT Vol 13, No 2 (2024)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/format.2024.v13.i2.010

Abstract

The increasing development of information technology makes it easy for people to get various information only through social media such as Twitter. Twitter is a mainstay social networking application and source of information on world events. With Twitter, people can get a lot of the latest news. One piece of information that is widely discussed and is a trending topic on Twitter is the impact of the Israeli and Palestinian war. It is important to analyze the feelings of the impact of the ceasefire between Israel and Palestine from the amount of information in online media. The data used is Twitter, a social media platform. This research was conducted to analyze people's reactions to data in the form of tweets and group them according to the Naïve Bayes method into positive, neutral or negative opinions. In implementing the Naïve Bayes algorithm which uses 3 models of the Naïve Bayes algorithm, namely Gaussian, Multinomial, and Bernoulli, it shows different results, namely 50% for the Naïve Bayes Gaussian model, 57% for the Naïve Bayes Bernoulli model, and Naïve Bayes Multinomial model is 65 %. This shows that the Multinomial Naïve Bayes model is better than other models in classifying the data in this case.
Perbandingan Algoritma Machine Learning Untuk Prediksi Gagal Bayar Pinjaman Koperasi yang Optimal Aziz, Hilmi; Rianto, Rianto
FORMAT Vol 13, No 2 (2024)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/format.2024.v13.i2.001

Abstract

Abstract - Predicting loan repayment defaults is quite an important thing to do in a financial institution such as a Savings and Loans Cooperative. The aim is to minimize the occurrence of loan defaults by borrowers to cooperatives so that bankruptcy does not occur. In this study, the development of a predictive model was carried out using several popular machine learning algorithms, namely logistic regression, decision tree, random forest and k-nearest neighbors (KNN), then the four models were compared and evaluated in order to find out which model with the most effective algorithm. in predicting loan defaults in cooperatives. Program evaluation is carried out by metrics such as accuracy, precision, recall, and f1-score. The dataset itself is obtained from a loan list which includes attributes such as borrower profile, loan amount, number of installments, etc. This dataset is divided into training data and test data to train and evaluate the model. The results showed that the Random Forest algorithm model provided the best accuracy, reaching 89%, followed by the Decision Tree with the highest accuracy value, which reached 84%, and finally Logistic Regression and K-Nearest Neighbors with the same accuracy value, namely 81%. These four algorithms were chosen because they are well-known algorithms among other algorithms for financial predictions because of their ability to understand complex relationships, provide interpretable results, overcome overfitting problems, and consider the interrelationships between similar entities. Abstrak – Melakukan prediksi kegagalan pembayaran pinjaman merupakan hal yang cukup penting untuk dilakukan di sebuah badan keuangan seperti Koperasi Simpan Pinjam. Tujuannya yaitu untuk meminimalisir terjadinya gagal bayar pinjaman oleh peminjam kepada Koperasi agar tidak terjadi bangkrut. Pada penelitian ini dilakukan pengembangan model prediksi dengan menggunakan beberapa algoritma machine learning yang cukup popular yaitu  logistic regression, decision tree, random forest dan k-nearest neighbors (KNN), kemudian keempat model tersebut dibandingkan dan dievaluasi agar diketahui model dengan algoritma mana yang paling efektif dalam memprediksi gagal bayar pinjaman di Koperasi. Evaluasi program dilakukan metrik-metrik seperti akurasi, presisi, recall, dan f1-score. Untuk datasetnya sendiri didapat dari daftar pinjaman yang mencakup atribut seperti profil peminjam, jumlah pinjaman, banyak angsuran, dll. Dataset ini dibagi menjadi data pelatihan dan data uji untuk melatih dan mengevaluasi model. Hasil penelitian menunjukkan bahwa model algoritma Random Forest memberikan akurasi terbaik yaitu mencapai 89%, diikuti oleh Decision Tree dengan nilai akurasi tertingginya yang mencapai 84%, dan yang terakhir Logistic Regression dan K-Nearest Neighbors dengan nilai akurasi yang sama yaitu 81%. Keempat algoritma ini dipilih karena merupakan algoritma yang cukup terkenal di antara algoritma lainnya untuk prediksi dalam hal keuangan karena kemampuan mereka untuk memahami hubungan yang kompleks, memberikan hasil yang dapat diinterpretasikan, mengatasi masalah overfitting, dan mempertimbangkan keterkaitan antara entitas yang serupa.
Studi Tentang Algoritma C5.0 Dalam Memprediksi Kepatuhan Nasabah Dalam Membayar Pajak Pertambahan Nilai Aprihartha, Moch Anjas; Husniyadi, M.; Alam, Taufik Nur
FORMAT Vol 13, No 1 (2024)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/format.2024.v13.i1.001

Abstract

Data mining is the process of extracting valuable patterns, information, and knowledge from large data sets. Data mining has an important role in identifying and minimizing risks in various lives. One of the algorithms of data mining is the decision tree type C5.0. The C5.0 algorithm is an algorithm used to solve classification problems. The C5.0 method can be applied in various sectors such as the taxation sector. Paying taxes is an obligation by an individual or entity paid to the State. The value added tax is the highest contributory tax because it is collected several times to companies. Factors that affect customer compliance in paying value added tax are income, entity form, and reporting status. This study aims to predict public compliance in paying value added tax using the C5.0 method. This research aims to produce a classification model that can be a potential solution for dealing with prediction problems in customer compliance with paying taxes. The results of the study obtained an average accuracy of the C5.0 model of 66,5%. Based on this accuracy value, the model can be categorized as still weak in predicting the status of value added tax payments.

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