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All Journal Jurnal Ilmiah Informatika Komputer Ekonomi Bisnis Jurnal Ilmiah Teknologi dan Rekayasa Jurnal Ilmiah KOMPUTASI JURNAL INSTEK (Informatika Sains dan Teknologi) Jurnal Informatika Universitas Pamulang JURNAL PERTANIAN PRESISI (JOURNAL OF PRECISION AGRICULTURE) ILKOM Jurnal Ilmiah Jurnal EDUCATIO: Jurnal Pendidikan Indonesia Jurnal Ilmiah Teknik (JUIT) International Journal Science and Technology (IJST)
Rama Dian Syah
Universitas Gunadarma
Agriculture, Biological Sciences & Forestry Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology Social Sciences

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IMPLEMENTASI SISTEM TUTORIAL KEDOKTERAN BERBASIS WEB DI FAKULTAS KEDOKTERAN UNIVERSITAS GUNADARMA Gunawan, Bhakti; Syah, Rama Dian; Zulenka, Sofian; Setyawati, Rena Fuji Erin; Suhatril, Ruddy J
Jurnal INSTEK (Informatika Sains dan Teknologi) Vol 9 No 2 (2024): OCTOBER
Publisher : Department of Informatics Engineering, Faculty of Science and Technology, Universitas Islam Negeri Alauddin, Makassar, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24252/instek.v9i2.48957

Abstract

Proses pembelajaran tutorial (problem based learning) memiliki critical insidents yang membuat pelaksanaan pembelajaran tutorial belum optimal. Tujuan dari penelitian ini adalah mengimplementasikan sistem tutorial berbasis web untuk pelaksanaan pembelajaran tutorial. Penelitian ini menggunakan metode waterfall untuk membuat sistem. Tahapan metode waterfall adalah pengumpulan kebutuhan, desain sistem, implementasi sistem, pengujian sistem, dan pemeliharaan sistem. Hasil dari penelitian ini yaitu sistem tutorial berbasis web di fakultas kedokteran universitas gunadarma. Pengujian sistem menggunakan metode blackbox menunjukkan semua fitur menu yang disediakan sistem berhasil berjalan dengan baik serta mahasiswa dan tutor berhasil melakukan pemakaian sistem. Sistem tutorial berbasis web ini membantu pelaksanaan pembelajaran tutorial menjadi lebih optimal.
KLASIFIKASI CITRA DIGITAL TULISAN TANGAN ANGKA MENGGUNAKAN METODE CONVOLUTIONAL NEURAL NETWORK Antonius Angga Kurniawan; Rama Dian Syah; Rizki Ariyani
Jurnal Ilmiah Teknik Vol. 1 No. 1 (2022): Januari : Jurnal Ilmiah Teknik
Publisher : Asosiasi Dosen Muda Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56127/juit.v1i1.1718

Abstract

Rapid technological advances have led to the development of computer vision science in various fields. This research aims to detect handwriting using deep learning technology with the Convolutional Neural Network (CNN) method. The research stages are data sample selection, data preprocessing, data training, data testing, and evaluation of results. This research succeeded in detecting handwriting with an accuracy value of 0.9800 and a loss value of 0.0665. There are several classification errors because images with numbers are less clear and almost look like numbers that they should not be. The more training data, the more the network will learn so that the accuracy will be better.
APLIKASI ENKRIPSI CITRA DIGITAL BERBASIS CHAOS MENGGUNAKAN ALGORITMA ARNOLD’S CAT MAP Rama Dian Syah; Antonius Angga Kurniawan; Rizki Ariyani
Jurnal Ilmiah Teknik Vol. 1 No. 2 (2022): Mei : Jurnal Ilmiah Teknik
Publisher : Asosiasi Dosen Muda Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56127/juit.v1i2.1719

Abstract

The rapid development of technology can lead to vulnerabilities in data and information. Everyone can access that data and information and disseminate it easily through the internet. The data and information can be in the form of text, video, audio, and images that may be confidential. To prevent misuse and unauthorized access to this confidential data and information, a technique is needed to enhance the security of the data. One of these techniques is by encrypting data. This technique is used to encode data in such a way that the security of the information is maintained and it cannot be read without being decrypted first. The encryption technique that has been developed involves implementing chaos theory, one of which uses the Arnold’s Cat Map algorithm. This algorithm is applied in the process of encrypting and decrypting digital images with png and bmp extensions. The results of this trial show that the image files can be encrypted and decrypted properly. The time obtained is directly proportional to the size of the image.
MODEL DEEP LEARNING UNTUK ANALISIS PREDIKSI HARGA SAHAM MENGGUNAKAN METODE LONG SHORT TERM MEMORY (LSTM) Luthfi, Muhammad Ruhunul; Syah, Rama Dian
Jurnal Ilmiah Ekonomi Bisnis Vol 30, No 1 (2025)
Publisher : Universitas Gunadarma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35760/eb.2025.v30i1.11870

Abstract

Investor yang berinvestasi saham selalu dituntut untuk menganalisis pasar saham agar dapat meminimalkan risiko kerugian dan meningkatkan keuntungan. Pemanfaatan teknologi Deep Learning dengan metode Long Short-Term Memory (LSTM) dapat digunakan untuk membantu analisis prediktif oleh para investor saham. Penelitian ini bertujuan untuk memprediksi harga saham PT Astra International Tbk menggunakan model Deep Learning metode Long Short Term Memory. Metode penelitian ini terdiri dari persiapan data, preprocessing data, pelatihan model, denormalisasi data, visualisasi hasil prediksi, dan evaluasi hasil prediksi. Visualiasi data menunjukkan bahwa model LSTM mampu memprediksi dengan cukup akurat dan mampu menangkap pola tren harga saham. Evaluasi model LSTM menujukkan nilai MAE mencapai 87.69 dengan persentase MAE mencapai 1.44%, nilai RMSE mencapai 116.87 dengan persentase RMSE mencapai 1.92%, dan nilai MAPE mencapai 1.45%.
IMPLEMENTASI METODE BIDIRECTIONAL ENCODER REPRESENTATIONS FROM TRANSFORMERS UNTUK ANALISIS SENTIMEN TERHADAP ULASAN APLIKASI ACCESS Tri Buwono Bagus Wicaksono; Rama Dian Syah
Jurnal Ilmiah Informatika Komputer Vol 29, No 3 (2024)
Publisher : Universitas Gunadarma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35760/ik.2024.v29i3.12514

Abstract

Technological developments in this digital era are growing rapidly in various fields, one of which is the field of public transportation. The purpose of this study is to conduct a sentiment analysis of Access by KAI application users on the Google Play Store so that it can be used as a suggestion to improve the quality of the application. This paper uses the Bidirectional Encoding Representations from Transformers (BERT) method with the pretrained IndoBERT model to train the Indonesian dataset. This writing method uses the CRISP-DM method with 6 stages, namely Business Understanding, Data Understanding, Data Preparation, Modelling, Evaluation, and Deployment. The dataset used was 10,000 reviews and after being processed into 9260. The model that was built managed to predict sentiment quite well with a percentage of 85%. However, in neutral sentiment, the number of wrong predictions was more than the number of correct predictions, which was 22 reviews, and the number of wrong predictions, which was 150 reviews. The number of correct predictions for negative sentiment is 2,822 reviews and the number of wrong predictions is 345 reviews. The number of correct predictions for positive sentiment was 234 reviews and the number of wrong predictions was 131 reviews. The model has also been successfully deployed in the form of a website prototype and can strengthen sentiment predictions quite well.
Pemanfaatan Learning Management System (LMS) dalam penyelenggaraan dan analisis progress test Lestari, Winda; Setyawati, Rena Fuji Erin; Gunawan, Bhakti; Suhartini, Sri Mukti; Hermita, Matrissya; Suhatril, Ruddy J; Syah, Rama Dian
Jurnal EDUCATIO: Jurnal Pendidikan Indonesia Vol 11, No 1 (2025): Jurnal EDUCATIO: Jurnal Pendidikan Indonesia
Publisher : Indonesian Institute for Counseling, Education and Therapy (IICET)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29210/1202525639

Abstract

Penilaian kemampuan mahasiswa kedokteran melalui progress test merupakan komponen penting dalam evaluasi pembelajaran berbasis Problem-Based Learning (PBL), namun pelaksanaan progress test secara konvensional memerlukan sumber daya dan upaya besar untuk menyediakan umpan balik indivual secara komprehensif. Penelitian ini bertujuan untuk mengevaluasi efektivitas Learning Management Systems (LMS) V-Class sebagai media penyelenggaraan dan analisis progress test dalam konteks assessment for learning pada mahasiswa kedokteran. Penelitian ini menggunakan pendekatan kuantitatif dengan desain kuasi eksperimental. Partisipan penelitian ini sejumlah 139 mahasiswa Fakultas Kedokteran Universitas Gunadarma Angkatan 2018-2021 yang telah mengikuti pembelajaran minimal dua semester pertama. Instrumen penelitian berupa soal progress test berbasis blueprint kurikulum yang diinput ke dalam LMS. Data hasil progress test diambil dari V-Class, kemudian dilanjutkan analisis butir menggunakan Microsoft Excel, serta evaluasi capaian belajar mahasiswa berdasarkan sistem menggunakan Uji Kruskal Wallis dan dilanjutkan dengan analisis Uji Wilcoxon. Hasil menunjukkan bahwa LMS mampu menyajikan total skor dan ketepatan jawaban per soal per mahasiswa, namun belum menyediakan analisis mendalam per blok pembelajaran. Rata-rata capaian tertinggi ditemukan pada topik komunikasi (66,67-100%) dan anatomi-patologi (49,24%-61,54%). Terdapat perbedaan rerata signifikan antar angkatan pada sejumlah topik (p<0,05). Penelitian ini menyimpulkan bahwa LMS memiliki potensi sebagai media assessment for learning, namun perlu pengembangan fitur analisis individual dan umpan balik berbasis topik. Temuan berdasar hasil penelitian ini berkontribusi pada pengembangan sistem evaluasi pembelajaran kedokteran berbasis teknologi dan mendukung efektivitas assessment for learning jangka panjang
A Deep Learning Approach for Tourism Destination Recommendation Using IndoBERT and TF-IDF Silfianti, Widya; Syah, Rama Dian; Suhendra, Adang; Isra, Ali; Darmayantie, Astie; Ohorella, Noviawan Rasyid
ILKOM Jurnal Ilmiah Vol 17, No 3 (2025)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v17i3.3069.241-251

Abstract

The rapid development of information technology has transformed various sectors, including tourism, where recommendation systems play a vital role in providing personalized services. Tourists are often faced with a wide range of destination choices, making decision-making increasingly complex. To address this, Artificial Intelligence (AI) and Natural Language Processing (NLP) can be leveraged to enhance recommendation accuracy through deeper analysis of destination descriptions. This study proposes a tourism destination recommendation system combining IndoBERT, SimCSE, and TF-IDF methods. IndoBERT was applied to capture semantic and contextual meaning in the Indonesian language, SimCSE improved sentence-level embeddings, and TF-IDF extracted essential keywords from descriptions. The system was implemented on a website to generate personalized recommendations based on user input. Evaluation results demonstrated that the composition of IndoBERT and TF-IDF achieved strong performance, with precision, recall, and F1-score values of 1.0 at a similarity threshold of 0.20. However, higher thresholds reduced recall and F1-score, indicating that a lower threshold provided a better balance between accuracy and coverage. The recommendation outputs matched user preferences, and functional testing showed that all website features performed successfully. These findings highlight the effectiveness of combining semantic and keyword-based methods for tourism recommendation. Future work could expand the dataset, integrate user feedback, and benchmark against other state-of-the-art models to further enhance system performance.
Co-Authors Adang Suhendra Ahmad Apandi Ahmad Hidayat Antonius Angga Kurniawan Bhakti Gunawan Darmayantie, Astie Genoveva, Zahwa Isra, Ali Istini, Syalis Ibnih Melati lestari, winda Luthfi, Muhammad Ruhunul Matrissya Hermita Naura Zainaty Rania Ohorella, Noviawan Rasyid Rena Fuji Erin Setyawati Rizki Ariyani Ruddy J Suhatril Sarifuddin Madenda Silfianti, Widya Sri Mukti Suhartini Suhatril, Ruddy J Suryadi Harmanto Tri Buwono Bagus Wicaksono Zulenka, Sofian