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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.
Arjuna Subject : -
Articles 330 Documents
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Kombinasi Analytical Hierarchy Process dengan Weighted Product untuk Penerima Beasiswa Prestasi Sistem Pendukung Keputusan Fathony, Zamzam; Saputra, Eko Rachmat Slamet .H; Frobenius, Arvin Claudy
Jurnal Transformatika Vol. 21 No. 2 (2024): Januari 2024
Publisher : Jurusan Teknologi Informasi Universitas Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26623/transformatika.v21i2.6815

Abstract

Scholarships are an appreciation given by universities in the form of educational assistance, one of which is for prospective students who have achievements in non-academic fields. Mercu Buana University Jakarta provides as many as 20 quotas per year for merit scholarships. The decision-making process for merit scholarship recipients is still focused on manual calculation using the average value method process. Based on these problems, a study was conducted to design a decision support system using analytical hierarchy process and weighted product methods. Variables used in achievement weighting, level, test scores. The process of weighting the AHP method produces an achievement priority value of 0.260, a level of 0.633, and a test score of 0.106 and the results on the consistency criteria matrix are 0.033. The results of the WP ranking are the scores on the achievement criteria, namely -0.260, the level is 0.633 and the test score is 0.106. The results on the user acceptance test are 84.4%, it can be concluded, functionality can be accepted by users
Implementasi Data Mining Untuk Estimasi Produktivitas Kacang Hijau Dengan Menggunakan Algoritma Regresi Linier Di Kabupaten Grobogan Kusumaningrum, Yulinda; Asmiatun, Siti; Putri, Astrid Novita
Jurnal Transformatika Vol. 19 No. 1 (2021): July 2021
Publisher : Jurusan Teknologi Informasi Universitas Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26623/transformatika.v19i1.9450

Abstract

The Grobogan Regency Agriculture Service is an agency that operates in the agricultural sector. One of the crop commodities in Grobogan Regency is green beans. Judging from the results obtained each year, green bean production in Grobogan Regency is inconsistent. The rise and fall of green bean productivity is influenced by several factors. Factors such as area, production, number of farmers and productivity can be estimated to determine the production of green beans in Grobogan Regency. Therefore, using the Multiple Linear Regression algorithm is expected to help to obtain results on how much green bean production is in Grogoban Regency as a reference for farmers to increase their green bean harvest each year. Based on the calculation results, it was found that the estimated productivity of green beans in Grobogan Regency reached 760.8297302 Tons/Ha, whereas previously the land was 865 Hectares (Ha) Keywords: data mining, linear regression, estimation, productivity, green beans
Prediksi Kepuasan Mahasiswa Terhadap Pelayanan Akademik Menggunakan Model Decision Tree Zaman, Badroe; huizen, lenny margaretta; Ardima, Muhammad Basyier
Jurnal Transformatika Vol. 21 No. 2 (2024): Januari 2024
Publisher : Jurusan Teknologi Informasi Universitas Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26623/transformatika.v21i2.8214

Abstract

Perguruan Tinggi merupakan sebuah lembaga pendidikan dimana didalamnya mempunyai tugas dalam pelayanan akademik. Kepuasan mahasiswa dalam memperoleh pelayanan akademik  merupakan hal yang sangat penting dalam menilai sebuah Perguruan Tinggi. Tujuan dari penelitian ini adalah agar dapat mengetahui bagaimana tingkat kepuasan mahasiswa program studi Teknik Informatika dalam hal memperoleh pengajaran oleh dosen, mengenai sarana dan prasarananya. Metode klasifikasi dan prediksi yang digunakan pada penelitian ini diambil dari salah satu model Decision Tree yaitu algoritma C4.5. Algoritma C4.5 berfungsi untuk mengekspolari data, menemukan hubungan tersembunyi antara sejumlah calon variabel input dengan sebuah variabel target. Hasil pengukuran yang didapat adalah nilai akurasi sebesar 94,23%. Nilai recall dari setiap kelas sebesar 94,12% untuk kelas Ya dan 100% untuk kelas Tidak. Sedangkan nilai presisi setiap kelas adalah sebesar 100% untuk kelas Ya dan 25% untuk kelas Tidak.
Pengembangan Media  Pembelajaran Statistika dan Probabilitas Berbasis Aplikasi Android Untuk Mendukung  Pembelajaran Hybrid Wicaksana, Dinar Anggit; Maulana, Charis; Gunata, Krida Pandu
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.v22i1.10230

Abstract

Pada masa new normal membuat pembelajaran hybrid menjadi salah satu yang wajib dimanfaatkan dalam melakukan transfer ilmu pengetahuan kepada mahasiswa. Berdasarkan hasil ujian statistika dan probabilitas semester genap tahun 2023 menunjukkan bahwa 55% mahasiswa menerima nilai cukup (C). Mahasiswa masih kesulitan dalam menganalisis uji hipotesis, analisis regresi dan korelasi. Penelitian ini bertujuan menghasilkan produk media pembelajaran statistika berbasis android yang dapat mendukung pembelajaran hybrid. Jenis penelitian ini adalah model Research and Development. Produk divalidasi oleh dua ahli yaitu ahli materi dan ahli media pembelajaran. Instrumen yang digunakan berupa angket dengan menggunakan skala likert. Diperoleh hasil validasi untuk produk media pembelajaran statistika mahasiswa teknik informatika universitas semarang adalah sebesar 3,35 dengan kategori sangat valid.
Identifikasi Penyakit Jantung Menggunakan Machine Learning: Studi Komparatif sintiya, endah septa; Rizdania, Rizdania; Afrah, Ashri Shabrina
Jurnal Transformatika Vol. 21 No. 2 (2024): Januari 2024
Publisher : Jurusan Teknologi Informasi Universitas Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26623/transformatika.v21i2.7144

Abstract

Heart disease is the number one cause of death globally. This condition is followed by an unhealthy lifestyle. Heart disease prediction needs to be done considering the importance of health. The presence of machine learning has made it easier for humans to make early detection of patterns approaching heart disease. This study compares 6 machine learning methods for disease classification with KNN, Naïve Bayes, Decision tree, Random forest, logistic regression, and SVM. The final classification obtained ranking accuracy with the highest value in the KNN method with precision, accuracy, re-call, fi-score tests. It is hoped that these results can be applied to real case studies of heart disease.
An Examination of Negative Correlations Using Pearson Correlation Analysis to Optimize the Diversification of Cryptocurrency Portfolios Widodo, Edi Widodo,; Rahmawati, Eka Putri; Bilqist, Chay Shona
Jurnal Transformatika Vol. 21 No. 2 (2024): Januari 2024
Publisher : Jurusan Teknologi Informasi Universitas Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26623/transformatika.v21i2.8095

Abstract

The purpose of this study is to employ the Pearson correlation approach in order to assess the association between different types of cryptocurrencies. The dataset included in this research comprises daily peak price information for 10 distinct categories of cryptocurrencies with the biggest market capitalizations from October 1, 2017 to December 31, 2022. Assessing and computing the correlation between cryptocurrency pairs with the Pearson correlation coefficient is the objective. The information utilized in this study was acquired from the website www.coinmarketcap.com. Pairs of stablecoins and crypto coin assets have the largest negative correlation, according to the findings of this study, in contrast to pairs of crypto currency assets. The pair ETH-BNB has the strongest positive correlation with a value of 0.948, while the pair LTC-USDT has the most negative correlation at -0.347. In order to replicate the impact of the negative correlation on trading activities, an exchange simulation was performed between the LTC and USDT pairings. Based on the outcomes of the simulation, the asset rise resulting from the exchange of the LTC and USDT pair from January 1, 2022 to December 31, 2022 was 12.09 percent. During the same time period, the asset's value would have declined by -48.69 percent if LTC was held. Conversely, an expansion of the time period from October 1, 2017 to December 31, 2022 yields an asset gain of 251,047.85 percent as a consequence of the exchange between LTC and USDT. Those individuals interested in reducing risk and diversifying their portfolios with cryptocurrency investments may find this information highly beneficial. The results of this research offer significant contributions to the current body of literature on bitcoin investment and offer investors valuable information
Sistem Rekomendasi Film pada Platform Streaming Menggunakan Metode Content-Based Filtering Azri Saputra, Jeremia Maheswara; Huizen, Lenny Margaretta; Arianto, Dede Brahma
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.v22i1.7041

Abstract

Sistem rekomendasi merupakan sebuah metode yang digunakan untuk memberikan rekomendasi pada sebuah produk seperti buku, musik dan film dengan memberikan nilai prediksi tertinggi pada penggunanya. Sistem Rekomendasi Film adalah sebuah sistem yang dapat memberikan rekomendasi-rekomendasi Film kepada pengguna sesuai dengan minat tonton pengguna. Jurnal ini dibuat dengan tujuan menciptakan Sistem Rekomendasi Film untuk membantu orang-orang dalam mencari Film yang mirip dengan Film kesukaan mereka. Rekomendasi Film dibuat dengan cara mencari kesamaan dari suatu Film, seperti genre nya.
Sistem Pendukung Keputusan Penyesuaian Nutrisi Makanan Berdasar Rekam Medis Pasien Berbasis Forward Chaining Setiawan, Aries; Setijaningsih, Retno Astuti; Ratnawati, Juli; Agiwahyuanto, Faik; Farida, Ida; Ashari, Ayu; Prasetya, Jaka
Jurnal Transformatika Vol. 21 No. 2 (2024): Januari 2024
Publisher : Jurusan Teknologi Informasi Universitas Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26623/transformatika.v21i2.8211

Abstract

Fulfilling nutrition for patients certainly does not only pay attention to the last illness they suffered, but nutritionists also need to pay attention to the patient's medical history. Providing certain foods to support the body's recovery after treatment for certain diseases may not necessarily be in accordance with the history of previous illnesses. Fulfilling nutritional intake according to certain disease conditions is not easy, especially if the patient has a medical history with a variety of diseases, so nutritionists need to be more selective in providing nutritional intake from a number of alternative foods that will be provided. A management decision system based on artificial intelligence is able to choose a food balance that is balanced with the various complaints experienced by patients. The method used in the food management information system for medical records uses the forward chaining method, namely by determining forward, in this case, food nutritional information that is suitable for the patient, by reading the facts that have been arranged as a representation to produce a conclusion. The accuracy value resulting from comparing manual nutrient selection and using forward chaining was 86%
Credit Risk Modeling Based on Geographical Location: A Case Study of Savings and Loan Cooperatives Widodo, Edi Widodo,; Rifai, Ahmad; Buana, Pratama Angga
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.v22i1.9710

Abstract

The aim of this study is to examine how geographical location affects the credit risk faced by savings and loan cooperatives. Using a quantitative approach, this research will develop a credit risk model that considers geographical variables,measured by the Human Development Index (HDI). The initial stage of the research involves classifying the credit dataset according to the categoriesdetermined by Bank Indonesia. The data cleansing process resulted in attributes such as credit ceiling, HDI, and credit category. Analysis was conducted using Chi-Square, and Logistic Regression methods. The Chi-Square analysis results showed  statistically significant relationship between credit ceiling, HDI, and credit category (p-value < 0.05). The Logistic Regression models demonstrated high accuracy in classifying the data, with Logistic Regression achieving 89.71%. In conclusion, credit ceiling and HDI have a significant influence on credit category, with the Logistic Regression model data classification. This study provides valuableinsights into how credit ceiling and HDI influence credit categories, which can be used to make better decisions related to public policy, developmentplanning, and social interventions
Analisa Forensik Kontainer Podman Terhadap Backdoor Metasploit Menggunakan Checkpointctl Sya'bani, Hafiidh Akbar; Umam, Chaerul; Handoko, L Budi
Jurnal Transformatika Vol. 21 No. 2 (2024): Januari 2024
Publisher : Jurusan Teknologi Informasi Universitas Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26623/transformatika.v21i2.8109

Abstract

Container systems are type of virtualization technology with isolated environment. The isolated environment in container system does not make cyber attacks impossible to occur. In this research, containers in which a cyber incident occurred were forensically tested on the container's memory to obtain digital evidence. The forensic process is carried out using standards from NIST framework with the stages of collection, examination, analysis and reporting. The forensic process begins by performing a checkpoint on the container to obtain information from the container's memory. In Podman the checkpoint process is carried out on one of the containers and will produce a file in .tar.gz form, where this file contains the information contained in the container. After the checkpoint process is complete, forensics is then carried out by reading the checkpoint file using a tool called checkpointctl. Forensic results showed that the container was running a malicious program in the form of a backdoor with a PHP extension.

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