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Bernadus Very Christioko
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Fakultas Teknologi Informasi dan Komunikasi, Universitas Semarang, Jl. Soekarno-Hatta Tlogosari Semarang, Central Java, Indonesia
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Jurnal Transformatika
Published by Universitas Semarang
ISSN : 16933656     EISSN : 24606731     DOI : https://doi.org/10.26623/transformatika
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.
Arjuna Subject : -
Articles 344 Documents
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Sistem Pakar Diagnosis Hama Dan Penyakit Tanaman Jeruk Keprok Menggunakan Metode Dempster Shafer Berbasis Web Buala, Adi Saldi
Jurnal Transformatika Vol. 22 No. 1 (2024): July 2024
Publisher : Jurusan Teknologi Informasi Universitas Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26623/transformatika.v22i1.9084

Abstract

Soe Tangerines are a type of local orange from the Soe area and are one of the leading agricultural commodities in East Nusa Tenggara (NTT) Province. The fruit is round and small with an average diameter of between 5-7 cm. Oranges are an important need for Indonesian people. Although the harvest of these oranges has not yet reached its maximum potential, this is caused by several factors such as cultivation techniques, environmental conditions, as well as pest and disease attacks. These three factors can cause a decrease in productivity and even crop failure. Therefore, an expert system was designed using the Dempster Shafer method. This method aims to detect pests and diseases based on the symptoms that appear on Tangerine plants and provide solutions to overcome these problems. The goal is to reduce the risk of pest and disease attacks on plants. The test results used 50 case data from the TTS Regency Agriculture and Plantation Service. The system test results from 50 data produced 42 case data that were in accordance with the expert diagnosis, 6 data that were in accordance with the expert diagnosis but below the threshold of 80% and 8 data that were not appropriate. Testing system accuracy by comparing system and expert diagnosis results obtained accuracy results of 84%.
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.
KLASIFIKASI SAMPAH ORGANIK  DAN NON ORGANIK MENGGUNAKAN TRANSFER LEARNING Huta Julu, Doly Ilham Saputra; Doly Ilham Saputra Huta Julu; Dewi Nurdiyah
Jurnal Transformatika Vol. 23 No. 1 (2025): July 2025
Publisher : Jurusan Teknologi Informasi Universitas Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26623/transformatika.v23i1.12201

Abstract

Pengelolaan sampah di Indonesia menghadapi tantangan serius dengan 7,2 juta ton sampah belum terkelola dengan baik dari 202 kabupaten/kota, mencemari lingkungan dan menghambat daur ulang berkelanjutan. Pemilahan sampah organik dan anorganik yang masih dilakukan secara manual rentan terhadap kesalahan manusia dan tidak efisien. Penelitian ini mengembangkan model klasifikasi sampah organik dan anorganik menggunakan metode transfer learning dengan tiga arsitektur CNN: VGG16, MobileNetV2, dan ResNet50V2. Dataset diambil dari kaggle Waste Classification Data yang telah melalui proses preprocessing. Hasil eksperimen menunjukkan bahwa MobileNetV2 unggul dengan akurasi 90,13%, presisi 96,25%, dan F1-Score 87,88%, waktu inferensi 127,76 ms. Arsitektur ini memberikan keseimbangan optimal antara performa tinggi dan efisiensi komputasi, sehingga ideal diterapkan pada perangkat pintar seperti ponsel dan sistem IoT dalam konteks manajemen sampah perkotaan. Penelitian ini menegaskan efektivitas transfer learning dalam membangun sistem klasifikasi sampah yang cerdas dan efisien untuk mendukung program pemilahan sampah di tingkat rumah tangga dan institusi.  
Komparasi AHP, SAW, TOPSIS, VIKOR, dan MABAC pada Sistem Pengambilan Keputusan Pemilihan Supplier Obat Purnomo Putro, Dwi; Eka Suryani, Puput; Amri, Saeful
Jurnal Transformatika Vol. 23 No. 1 (2025): July 2025
Publisher : Jurusan Teknologi Informasi Universitas Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26623/transformatika.v23i1.12220

Abstract

The selection of pharmaceutical suppliers is crucial for ensuring consistent drug availability and maintaining service quality in healthcare facilities. This study offers a comparative analysis of five Multi Criteria Decision Making methods (AHP, SAW, TOPSIS, VIKOR, and MABAC) applied to supplier evaluation based on four key criteria: price, delivery time, receipt accuracy, and product quality. Unlike previous studies that employed individual or dual methods, this research evaluates all five methods using the same dataset to assess consistency, sensitivity, and decision reliability. The results show strong ranking consistency across methods, with AHP and SAW producing identical outputs. TOPSIS and VIKOR offer similar outcomes based on proximity and compromise analysis, while MABAC demonstrates high discrimination power for mid-ranked suppliers. Sensitivity tests confirm ranking stability under moderate weight variations. This study provides practical recommendations for selecting appropriate decision methods in pharmaceutical procurement systems based on operational context and desired decision accuracy.

Page 32 of 35 | Total Record : 344

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