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All Journal JOIV : International Journal on Informatics Visualization Jurnal Teknologi Terpadu Jurnal Teknologi Informasi Cyberku SARGA
Yonathan Purbo Santosa
Universitas Katolik Soegijapranata
Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science

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Klasterisasi Kecerdasan Majemuk Siswa Berbasis Jaringan Syaraf Kohonen Guna Mendukung Adaptive Elearning Stefanus Santosa; Wiji Lestari Panjidang; Yonathan Purbo Santosa
Jurnal Teknologi Informasi - Cyberku (JTIC) Vol 15 No 2 (2019): Jurnal Teknologi Informasi - Jurnal CyberKU Vol. 15, no 2
Publisher : Program Pascasarjana Magister Teknik Informatika, Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (433.09 KB)

Abstract

Learning strategies are often applied without considering the unique and different characteristics of the learner's intelligence. This causes students to have difficulty understanding the material, not focused, bored, decreased motivation, frustration, and various other learning difficulties. The efforts to create student-oriented learning strategies can be done with adaptive elearning. Adaptive elearning system requires recognition function to cluster the intelligence of the learner when learning takes place. This study shows that Kohonen's Artificial Neural Network can be used for mapping students based on multiple intelligences. The results showed that there were 8 clusters with different intelligence compositions. There is no cluster that has a single intelligence. Intrapersonal intelligence is almost owned by 90% of students, while the lowest is visual-spatial intelligence, which is only 23.33%. In order to create a learner-oriented learning process, this clustering method should be embedded in an adaptive elearning system.
Computational of Concrete Slump Model Based on H2O Deep Learning framework and Bagging to reduce Effects of Noise and Overfitting Stefanus Santosa; Yonathan P. Santosa; Garup Lambang Goro; - Wahjoedi; Jamal Mahbub
JOIV : International Journal on Informatics Visualization Vol 7, No 2 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.2.1201

Abstract

Concrete mixture design for concrete slump test has many characteristics and mostly noisy. Such data will affect prediction of machine learning. This study aims to experiment on H2O Deep Learning framework and Bagging for noisy data and overfitting avoidance to create the Concrete Slump Model. The data consists of cement, blast furnace slag, fly ash, water, superplasticizer, coarse aggregate, fine aggregate, age, slump, and compressive strength. A primary data for concrete mixed design using the fine aggregate material from Merapi Volcano, the hills of Muntilan, and Kalioro. The coarse aggregate was obtained from Pamotan, Jepara, Semarang, Ungaran, and Mojosongo Boyolali Central Java. The cement was using Gresik and Holcim product and the water was from Tembalang, Semarang. The experiment model with one input layer with 7 neurons, one hidden layer with 20 neurons, and one output layer with 1 neuron using activation function TanH, with parameter L1=1.0E-5, L2=0.0, max weight=10.0, epsilon=1.0E-8, rho=0.99, and epoch=800 is able to achieve RMSE of 2.272. This result shows that after introducing Bagging, the error can be reduced up to 2.5 RMSE approximately (50% lower) compared to the model without Bagging. The manually tested mixture data was used to model evaluation. The result shows that the model was able to achieve RMSE 0.568. Following this study, this model can be used for further research such as creating slump design practicum equipment/ application software.
Kombinasi Linier Target Data Untuk Regresi Multitarget Menggunakan Principal Component Analysis Yonathan Purbo Santosa
Jurnal Teknologi Terpadu Vol. 9 No. 1 (2023): Juli, 2023
Publisher : LPPM STT Terpadu Nurul Fikri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54914/jtt.v9i1.516

Abstract

Linear regression is a method to predict numbers, a dependent variable (output) based on some independent variables (inputs). The problem with regression is that some data does not fall into linear problems. Based on this problem, RLC was invented to randomly find a correlation between output by projecting the data into the higher dimension. Unfortunately, RLC does not provide ways to inverse the projection, resulting in poor performance results. On top of that, projecting the data into a higher dimension will increase the learning algorithm complexity. Consequently, PCA can solve the problems by projecting the target data into a lower dimension while leaving possibilities for inverse transformation. This research was implemented with the help of the sci-kit-learn library to create and train the regression model and transform the dataset using Python programming language. As a result, for 12 datasets, augmentation using PCA achieved lower error in 7 datasets than RLC, averaging at 0.3270 for augmentation using PCA and 0.4003 for augmentation using RLC.
Pemanfaatan AI Generatif dalam Eksplorasi Arsitektur Tongkonan ke Desain Kontemporer Gustav Anandhita; Peter Ardhianto; Yonathan Purbo Santosa; Ratih Dian Saraswati; Christian Moniaga
SARGA: Journal of Architecture and Urbanism Vol. 20 No. 2 (2026): July 2026
Publisher : Universitas 17 Agustus 1945

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56444/sarga.v20i2.3719

Abstract

Penelitian ini bertujuan mengeksplorasi transformasi morfologi arsitektur tradisional Tongkonan Toraja ke dalam desain bangunan kontemporer menggunakan Generative AI. Arsitektur Tongkonan menghadapi tantangan adaptasi di tengah modernisasi tanpa kehilangan identitas budayanya. Penelitian ini menggunakan pendekatan eksperimental metode campuran (mixed-method) melalui kajian literatur terhadap anatomi struktural (Rattiang Banua, Kale Banua, dan Sulluk Banua) yang kemudian dikurasi menjadi dataset khusus untuk melatih model AI. Desain dievaluasi melalui kerangka kerja ganda, yakni pengujian komputasional Visual Question Answering (VQA) Score dan analisis arsitektural kualitatif. Hasil pengujian VQAScore menunjukkan kecocokan gambar dan teks perintah (prompt) yang tinggi, dengan tipologi Taman memperoleh skor tertinggi (96%) dan Istana Kepresidenan terendah (81%). Namun, analisis kualitatif mengungkap adanya risiko komodifikasi fasad; meskipun AI efektif mengadaptasi bentuk atap secara visual, filosofi tradisional rentan direduksi menjadi sekadar estetika parametrik dangkal tanpa menerjemahkan hierarki keruangan secara utuh. Selain itu, kelayakan tektonika struktural desain belum teruji secara fisik. Penelitian ini menyimpulkan bahwa teknologi AI generatif sangat potensial sebagai jembatan eksplorasi visual awal, namun penerapannya dalam preservasi budaya mutlak mensyaratkan validasi rekayasa struktur dan pelibatan pakar budaya. Kontribusi penelitian ini terletak pada pengembangan kebaruan metodologis melalui kerangka kerja evaluasi ganda (dual-evaluation framework) yang mengintegrasikan pengukuran komputasional VQA Score dan analisis arsitektural kualitatif, serta kurasi dataset morfologi spesifik untuk meminimalisasi bias visual AI dalam mendesain arsitektur vernakular.
Co-Authors - Wahjoedi Christian Moniaga Garup Lambang Goro Gustav Anandhita Jamal Mahbub Peter Ardhianto Ratih Dian Saraswati Stefanus Santosa Stefanus Santosa Wiji Lestari