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Jurnal Transformatika
Published by Universitas Semarang
ISSN : 16933656     EISSN : 24606731     DOI : -
Core Subject : Science,
Computer Science & IT
Transformatika is a peer reviewed Journal in Indonesian and English published two issues per year (January and July). The aim of Transformatika is to publish high-quality articles of the latest developments in the field of Information Technology. We accept the article with the scope of Information Systems, Web Technology, Computer Networks, Artificial Intelligence, and Multimedia.
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Articles 10 Documents
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Search results for , issue "Vol. 22 No. 2 (2025): January 2025" : 10 Documents clear
Decision Support System in Determining the right Investment Instrument Gunawan, Dedi
Jurnal Transformatika Vol. 22 No. 2 (2025): January 2025
Publisher : Jurusan Teknologi Informasi Universitas Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26623/gceske56

Abstract

The number of investment instrument is often confusing for people who have minimal knowledge but still want to investing. Due to the many investments instrument offered, with the lack of public literacy in financial management, most people have a fear of missing out which case the phenomena of FOMO (Fear of Missing Out), the desire of people to get rich quick without thinking about the risk future which result in many fraudulent practices circulating in the community. This study aims to assist users in deciding the right investment instrument according to the user s risk profile. The decision support system in determining investment instrument with AHP (Analytical Hierarchy Process) Method is a method for problems in determining priorities from various alternative. AHP will make it easier to determine the right instrument.    
Implementasi Metode Dempster-Shafer Untuk Mendiagnosis Karies Gigi Mauk, Imanuel Brian Calvin; Sina, Derwin Rony; Ledoh, Juan Rizky Mannuel
Jurnal Transformatika Vol. 22 No. 2 (2025): January 2025
Publisher : Jurusan Teknologi Informasi Universitas Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26623/cw57h274

Abstract

Gigi merupakan struktur keras yang terdapat di mulut manusia, berfungsi untuk mengunyah makanan, membantu dalam pengucapan, dan memberikan bentuk pada wajah. Gigi juga sering mengalami gangguan penyakit. Penyakit yang umumnya terjadi pada gigi adalah karies gigi. Penyebab dari karies gigi disebabkan karena kurangnya kebiasaan menyikat gigi secara teratur dan kurangnya kesadaran masyarakat akan pentingnya kesehatan gigi. Banyak orang yang mengalami karies gigi cenderung tidak langsung pergi ke dokter gigi, sehingga masalah pada email gigi bisa semakin parah, membentuk lubang atau karies yang lebih besar. Jumlah tenaga dokter gigi di Indonesia sangat terbatas, rasio antara dokter dengan jumlah pasien adalah 1:12000. Perkembangan teknologi saat ini ada suatu sistem yang dapat berkerja layaknya seorang pakar. Sistem pakar ini menggunakan perunutan maju (forward chaining) dan metode perhitungan nya menggunakan Dempster-Shafer. Dempster-Shafer merupakan metode ketidakpastian untuk menghasilkan suatu diagnosa yang berdasarkan nilai densitas dari setiap gejala. Penelitian dilakukan dengan membandingkan data hasil diagnosa pakar, pakar menetapkan batas nilai threshold sebesar 70%. Dari 50 data uji didapati 47 data yang sesuai dan diatas threshold sehingga didapati akurasi sistem sebesar 94%. Sementara itu dari 50 data uji ada 3 data uji yang berada di bawah nilai threshold
Penggunaan Feature Space SMOTE Untuk Mengurangi Overfitting Akibat Imbalance Dataset Kurniawan, Wira Adi; Salam, M.Kom, Abu
Jurnal Transformatika Vol. 22 No. 2 (2025): January 2025
Publisher : Jurusan Teknologi Informasi Universitas Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26623/transformatika.v22i2.8305

Abstract

The creation of a classification model requires careful consideration of several crucial factors to achieve optimal performance. A good model is typically indicated by high accuracy and F1-score values, as well as low loss values. To create a successful model, certain conditions must be met, including selecting the appropriate architecture and ensuring the availability of high-quality data. In this study, a classification model for CT Kidney Stone was developed using an imbalanced dataset obtained from Kaggle. The chosen algorithm for model development was Convolutional Neural Network (CNN), as CNN is known for its effectiveness in image classification tasks. Three different pre-processing approaches were employed in model creation. The first model was built using the imbalanced training data. The second model involved data augmentation, while the third model utilized SMOTE oversampling. Subsequently, all three models were evaluated using private data to assess testing performance and identify any potential overfitting. The research findings revealed that the third model exhibited the best performance among the three, showcasing its superiority in handling the imbalanced dataset and achieving optimal results.
Comparative Study of Information System Governance Frameworks: Foundations for IT Risk Management Using COBIT 2019 and ITIL Sholeh, Moch. Badrus; Pramudya, Naufal Daffa
Jurnal Transformatika Vol. 22 No. 2 (2025): January 2025
Publisher : Jurusan Teknologi Informasi Universitas Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26623/fh0vee39

Abstract

In this study, COBIT 2019 and ITIL V4 are compared in the context of managing IT risk. Through systematic literature review (SLR), the theoretical and practical foundations of both frameworks are evaluated. COBIT 2019 offers a structured approach, while ITIL emphasizes adaptive operational practices. Analysis of strengths and weaknesses helps organizations choose an approach that aligns with their strategic objectives. With this understanding, organizations can enhance their ability to manage IT risks and achieve business goals effectively.
Optimasi Clustering K-Means Menggunakan Algoritma Genetika Dengan Data View Dan Like Di Tiktok Setiaji, Galet Guntoro; Gunata, Krida Pandu; Setiarso, Galih
Jurnal Transformatika Vol. 22 No. 2 (2025): January 2025
Publisher : Jurusan Teknologi Informasi Universitas Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26623/y2tedy77

Abstract

K-Means merupakan algoritma yang sering digunakan untuk melakukan pengelompokkan atau sering juga disebut clustering. Dengan menentukan pusat centroid awal secara random pada algoritma K-Means akan ditingkatkan performanya menggunakan Algoritma Genetika (GA). Menggunakan data set publik di Kaglle, berupa data set tiktok dimana jumlah view dan like dengan record data sebanyak 19.084 setelah dilakukan pembersih data. Yang akan diuji dengan melakukan performa clustering K-Means dengan Algoritma Genetika. Dan untuk validitas nya nanti menggunakan Davis Boulden Index, dimana hasil validitas DBI ini nanti akan meningkatkan performance K-Means dengan menambahkan Algoritma Genetika. Dengan pengujian K-Means dengan jumlah k=3, k=4 dan k=5 menghasilkan masing-masing validitas DBI 0,64 ; 0,79 dan 0,72. Sedangkan untuk algoritma K-Means dengan peningkatan performa menggunakan GA didapatkan validitas dengan masing-masing DBI sebagai berikut 0,45 ; 0,40 dan 0,60. Dengan hasil penelitian menghasilkan bahwa peningkatan performa K-Means dengan menggunakan GA memberikan hasil validitas lebih kecil dari pada hanya menggunakan perhitungan KMeans saja.
Perbandingan Naïve Bayes dan K-NN dalam Analisis Sentimen Aplikasi X lona, ririn; Pandie, Emerensye S.Y. Pandie; Fanggidae, Adriana Fanggidae
Jurnal Transformatika Vol. 22 No. 2 (2025): January 2025
Publisher : Jurusan Teknologi Informasi Universitas Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26623/f4k55e04

Abstract

Aplikasi X, sebelumnya dikenal sebagai Twitter adalah media sosial yang memungkinkan pengguna mengirim, membalas, dan membaca pesan. Berdasarkan ulasan di Google Play Store, banyak pengguna mengeluhkan masalah, terutama terkait penangguhan akun setelah perubahan kepemilikan. Namun, sebagian pengguna masih merasa puas dan terbantu dengan X. Oleh karena itu, analisis sentimen dilakukan untuk mengetahui kecenderungan opini pengguna. Penelitian ini menggunakan metode naïve bayes dan k-Nearest Neighbor pada 8.723 ulasan yang kemudian diklasifikasi sebagai sentimen positif, netral, atau negatif menggunakan K-fold cross validation. Naïve Bayes mencapai akurasi tertinggi sebesar 88,87% pada 10-fold, sementara KNN dengan k optimal di 12-NN mencapai 90,32% pada 2-fold. Dalam perbandingan hasil klasifikasi dengan label pakar kedua, metode Naïve Bayes lebih sesuai dengan akurasi 92,56% dibandingkan KNN yang mencapai 91,73%.
Design and Development Optimized FIFO Queue System for Food Outlets Tanusaputra, Johan William; Budhi, Robby Kurniawan; Trisno, Indra Budi
Jurnal Transformatika Vol. 22 No. 2 (2025): January 2025
Publisher : Jurusan Teknologi Informasi Universitas Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26623/bb1w7w63

Abstract

In today's fast-paced food service industry, the efficiency of queue management is vital to operational success, profitability, and customer satisfaction. This study evaluates an integrated queue management system's impact on these critical areas. The results show an average satisfaction score of 80.11% from customers and 90.37% from food outlet owners, demonstrating the system's strong effectiveness. The research focused on the importance of reducing perceived waiting times through real-time updates, which enhance customer tolerance and satisfaction. By combining online and onsite ordering, the system provides real-time updates, order tracking, and notifications to boost efficiency and minimize cancellations. Despite some identified weaknesses, such as the absence of direct customer reviews and existing bugs, the system holds significant potential for improving user experience. These findings highlight the necessity for continuous development and maintenance to optimize the system further. Overall, this approach promises to advance the operational capabilities and customer satisfaction levels of food outlets.
Boosting Performance Klasifikasi kNN Customer Loyalty dengan Chi-Square dan Information Gain Mutiarachim, Atika; Fikriah, Fari Katul; Ansor, Basirudin; Ramdani, Aditya Putra
Jurnal Transformatika Vol. 22 No. 2 (2025): January 2025
Publisher : Jurusan Teknologi Informasi Universitas Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26623/6wgy1097

Abstract

Understanding customer purchasing behavior is essential for predicting customer loyalty, which directly impacts a company's long-term success. This research aims to determine the effect of chi-square and information gain feature selection in optimizing customer loyalty classification performance, compared to pure kNN. Using a public customer purchasing behavior dataset from Kaggle, containing 10,000 data, 12 attributes with loyalty_status as the label (Gold, Regular, Silver). Evaluating performance by accuracy, kappa, classification error, recall, precision, and RMSE. The highest accuracy 91.99% was obtained by kNN k=3 with information gain, kappa 0.844, precision 95.44%, recall 86.30%, with the lowest classification error 8.01% and the second lowest RMSE 0.245, after kNN k=3 with chi-square. Results show that feature selection has a positive impact on classification, increasing accuracy and reducing errors, with the combination of the kNN k=3 method and information gain proving successful in obtaining high accuracy in classifying customer loyalty.
Komparasi Metode SVM dan Adaboost untuk Klasifikasi Kanker Payudara Elfitrianna, Ikka Ayu; Prathivi, Rastri
Jurnal Transformatika Vol. 22 No. 2 (2025): January 2025
Publisher : Jurusan Teknologi Informasi Universitas Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26623/9adm2e13

Abstract

One of the most prevalent malignancies in women and a major global cause of death is breast cancer. To determine whether a cancer is benign or malignant, early detection is essential. The usefulness of the Support Vector Machine (SVM) and Adaptive Boosting (Adaboost) algorithms for breast cancer classification using mammography data is compared in this study. 569 records make up the dataset, which was sourced from the Kaggle Repository and is split into 75% training data and 25% testing data. Preprocessing steps include feature and target variable creation, categorical-to-numerical conversion, data splitting, and normalization. SVM achieved an accuracy of 97%, with a precision of 98%, recall of 94%, and F1 score of 96%. Adaboost, on the other hand, achieved an accuracy of 96%, precision of 98%, recall of 92%, and F1 score of 95%. The results reveal that both algorithms are highly effective for breast cancer detection, with SVM marginally exceeding Adaboost in total performance. These findings emphasize the promise of machine learning techniques in facilitating early cancer diagnosis, hence boosting survival rates. It is advised that future research employ a wider range of datasets and investigate different classification techniques in order to improve accuracy and dependability even more.
Pengaruh Penerapan Routing I-BGP Terhadap Waktu Failover Dalam Jaringan Lokal  Hartanto, Agus; Surono, Surono; Wicaksana, Dinar Anggit
Jurnal Transformatika Vol. 22 No. 2 (2025): January 2025
Publisher : Jurusan Teknologi Informasi Universitas Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26623/a165hw72

Abstract

Keandalan jaringan komputer menjadi elemen penting dalam mendukung berbagai sektor di era digital. Failover, mekanisme pengalihan otomatis ke jalur cadangan saat jalur utama gagal, sangat bergantung pada kecepatan respons dan kemampuan protokol routing. Penelitian ini mengevaluasi efektivitas Internal Border Gateway Protocol (I-BGP) dalam mempercepat waktu failover pada jaringan lokal berbasis Mikrotik yang terhubung melalui VPN. Performa I-BGP dibandingkan dengan OSPF, RIP, dan Static Routing melalui pengujian waktu failover, jumlah paket hilang, dan efisiensi bandwidth. Hasil menunjukkan I-BGP memiliki waktu failover tercepat (0,51 detik), kehilangan paket minimal (2 paket), dan utilisasi bandwidth tertinggi (95%). Uji ANOVA mengonfirmasi perbedaan signifikan antar protokol (F=776,898, p<0,001). Temuan ini menegaskan keunggulan I-BGP sebagai solusi optimal untuk failover cepat dan andal. Dimasa mendatang tantangan dan kompleksitas dari jaringan internet akan semakin besar, untuk itu perlu diadakan kajian komprehensif dan penelitian lebih lanjut tentang gangguan konektifitas yang berkaitan dengan faktor keamanan, dan solusi dengan penggunaan kecerdasan buatan.

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