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All Journal Jurnal Teknologi Informasi dan Ilmu Komputer Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Jurnal Teknologi Informasi, Komputer, dan Aplikasinya (JTIKA ) JATI (Jurnal Mahasiswa Teknik Informatika) Jurnal Abdi Insani JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Jurnal Teknik Informatika (JUTIF) Jurnal Begawe Teknologi Informasi AMPLITUDO: Journal of Science & Technology Innovation
Dwiyansaputra, Ramaditia
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Agriculture, Biological Sciences & Forestry Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics

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OPTIMALISASI LAYANAN SISTEM INFORMASI MAHASISWA DENGAN INTEGRASI TELEGRAM : CHATBOT RETRIEVAL-AUGMENTED-GENERATION BERBASIS LARGE LANGUAGE MODEL Hidayat, Lalu Ramdoni; Wijaya, I Gede Pasek Suta; Dwiyansaputra, Ramaditia
JTIKA (Jurnal Teknik Informatika, Komputer dan Aplikasinya) Vol 7 No 1 (2025): Maret 2025
Publisher : Program Studi Teknik Informatika, Fakultas Teknik, Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jtika.v7i1.459

Abstract

Kemajuan teknologi telah memberikan dampak yang cukup signifikan dalam berbagai bidang, termasuk salah satunya Pendidikan. Dalam aspek Pendidikan permasalahan yang dihadapi adalah keterbatasan akses mahasiswa terhadap informasi akademik secara cepat dan efisien. Untuk mengatasi hal ini, penelitian ini bertujuan mengembangkan chatbot berbasis Telegram yang mampu memberikan respons informatif, akurat, dan ringkas terhadap pertanyaan pengguna terkait akademik di program studi Teknik Informatika. Chatbot ini memanfaatkan metode Retrieval-Augmented-Generation (RAG) untuk memproses informasi dari dokumen teks secara efisien. Metode RAG digunakan untuk menemukan jawaban yang relevan dari dokumen berdasarkan pertanyaan pengguna, sementara Large Language Model memahami konteks pertanyaan dan menghasilkan jawaban yang sesuai. Penelitian ini menggunakan pendekatan Research and Development (R&D) dengan tahapan meliputi survei questioner kebutuhan mahasiswa, preprocessing data, Pembangunan indeks pencarian berbasis vektor, konfigurasi model LLM, serta integrasi chatbot dengan Telegram. Hasil pengujian menunjukkan bahwa chatbot mampu memberikan jawaban dengan akurasi tinggi dan waktu respons rata-rata 60 detik untuk pertanyaan sederhana hingga kompleks, sehingga chatbot berbasis RAG cukup efektif meningkatkan aksesibilitas informasi secara real-time. Pengembangan lebih lanjut dapat difokuskan pada peningkatan pemahaman terhadap beragam pertanyaan dan personalisasi respons.
PENDEKATAN SENTIMEN BERBASIS ASPEK PADA ULASAN SIRKUIT MANDALIKA MENGGUNAKAN CNN DAN REPRESENTASI FASTTEXT Manuaba, Ida Bagus Ryand Wirayana; Dwiyansaputra, Ramaditia; Hamidi, Mohammad Zaenuddin
JTIKA (Jurnal Teknik Informatika, Komputer dan Aplikasinya) Vol 7 No 1 (2025): Maret 2025
Publisher : Program Studi Teknik Informatika, Fakultas Teknik, Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jtika.v7i1.460

Abstract

Reviews are texts that contain an assessment or comment on something and can be used to provide more in-depth information. This research aims to analyze community reviews of the Mandalika Circuit using the aspect-based sentiment analysis technique CNN method. The CNN model is trained using two types of word embedding, namely Keras and FastText, and supported by the Multilabel Stratified K-Fold Cross Validation method to ensure an even distribution of data on each label and produce a stable accuracy evaluation. The results show that CNN with FastText word embedding has a higher average accuracy than CNN with Keras word embedding for both aspect and sentiment classification tasks. However, the model had difficulty in classifying the positive class in the sentiment label, which was due to the smaller amount of review data with positive sentiment than neutral and negative. Therefore, for future research, it is recommended to use data augmentation techniques on the imbalanced classes to improve the accuracy of the model.
Implementation of Authentication and Access Management Microtic-Based Internet for Visitors at NTB Regional Library: IMPLEMENTASI AUTENTIKASI DAN MANAJEMEN AKSES INTERNET BERBASIS MIKROTIK UNTUK PENGUNJUNG DI PERPUSTAKAAN DAERAH NTB Dewi, Zaskia Elvina; Dwiyansaputra, Ramaditia; Hirkan, Muhamad Nurul
Jurnal Begawe Teknologi Informasi (JBegaTI) Vol. 6 No. 2 (2025): JBegaTI
Publisher : Program Studi Teknik Informatika, Fakultas Teknik Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jbegati.v6i2.1441

Abstract

Kegiatan pengabdian masyarakat ini bertujuan untuk mengimplementasikan sistem autentikasi dan manajemen akses internet berbasis MikroTik di Perpustakaan dan Kearsipan Daerah Provinsi Nusa Tenggara Barat. Permasalahan utama yang dihadapi oleh pihak perpustakaan adalah tidak adanya sistem yang mampu memisahkan hak akses antara pengguna anggota dan non-anggota, serta belum adanya kontrol terhadap kecepatan dan durasi penggunaan internet. Melalui konfigurasi hotspot pada MikroTik RouterOS, dilakukan pengaturan User Profile yang membedakan bandwidth dan waktu akses untuk masing-masing jenis pengguna. Hasil implementasi menunjukkan sistem berjalan dengan baik, koneksi internet stabil, serta mampu meningkatkan efisiensi pengelolaan jaringan. Kegiatan ini juga memberikan kontribusi positif terhadap peningkatan keterampilan teknis dalam bidang jaringan komputer. Diharapkan sistem yang diterapkan dapat mendukung pelayanan teknologi informasi yang lebih tertib dan terstruktur di lingkungan perpustakaan. Keywords: Autentikasi, Hotspot, MikroTik, Manajemen Akses, Perpustakaan.
PERAN FRONT MASYARAKAT PEDULI LINGKUNGAN (FMPL) GILI TRAWANGAN MELALUI METODE PARTICIPATORY LEARNING AND ACTION (PLA) DALAM TATA KELOLA SAMPAH BERKELANJUTAN Ita Selvia, Siska; Widiyanti, Astrini; Dwiyansaputra, Ramaditia; Kusuma, Fendi Putra; Tiara, Baiq Najwa
Jurnal Abdi Insani Vol 12 No 9 (2025): Jurnal Abdi Insani
Publisher : Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/abdiinsani.v12i9.2847

Abstract

The waste problem on a small island, coupled with the pressure of popular tourism activities in Indonesia, has caused Gili Trawangan to face serious challenges in waste management and requires the active involvement of all relevant stakeholders. The Gili Trawangan Environmental Care Community Front (FMPL) is a self-help group. This community service activity aims to strengthen the role of the Gili Trawangan Environmental Care Community Front (FMPL) through the application of the Participatory Learning and Action (PLA) method in sustainable waste management. The PLA approach encourages the active participation of FMPL members in identifying problems, designing solutions, and implementing concrete actions appropriate to the local context. Activities are carried out through workshops, focus group discussions, and direct practice in the field. The results of the community service show an increase in the capacity of FMPL members in the aspects of planning, implementing, and evaluating waste management programs. In addition, a shared commitment is built among residents and local stakeholders to support a more sustainable waste management system. This activity positively contributes to strengthening community institutions and improving environmental quality in Gili Trawangan
PERFORMANCE ANALYSIS OF MULTILINGUAL AND MONOLINGUAL MODELS IN PREDICTING INDONESIAN LANGUAGE EMOTION USING TWITTER DATASET Paramarta, Muhammad Magistra Apta; Dwiyansaputra, Ramaditia; Rassy, Regania Pasca
JTIKA (Jurnal Teknik Informatika, Komputer dan Aplikasinya) Vol 7 No 2 (2025): September 2025
Publisher : Program Studi Teknik Informatika, Fakultas Teknik, Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jtika.v7i2.482

Abstract

Although Indonesia has the third largest population in the world, the number of datasets available in the field of text processing in Indonesian is still very limited. Therefore, this research utilizes the ability of multilingual models that can be trained with multiple languages to predict emotions based on low-resource language such as Indonesian. Several training scenarios were conducted to evaluate the transferability and performance of these multilingual models compared to the monolingual IndoBERT model. The experimental results show that XLM-R outperforms mBERT and achieves competitive performance to IndoBERT, with XLM-R and IndoBERT achieving F1-score of 0.7793 and 0.7733 respectively. XLM-R also demonstrates competitive results on other evaluation metrics. These findings suggest that XLM-RoBERTa could be a promising alternative for emotion detection in languages with limited resources, such as Indonesian.
Multiclass Text Classification of Indonesian Short Message Service (SMS) Spam using Deep Learning Method and Easy Data Augmentation Latifah, Nurun; Dwiyansaputra, Ramaditia; Nugraha, Gibran Satya
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 23 No. 3 (2024)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v23i3.3835

Abstract

The ease of using Short Message Service (SMS) has brought the issue of SMS spam, characterized by unsolicited and unwanted. Many studies have been conducted utilizing machine learning methods to build models capable of classifying SMS Spam to overcome this problem. However, most of these studies still rely on traditional methods, with limited exploration of deep learning-based approaches. Whereas traditional methods have a limitation compared to deep learning, which performs manual feature extraction. Moreover, many of these studies only focus on binary classification rather than multiclass SMS classification which can provide more detailed classification results. The aim of this research is to analyze deep learning model for multiclass Indonesian SMS spam classification with six categories and to assess the effectiveness of the text augmentation method in addressing data imbalace issues arising from the increased number of SMS categories. The research method used were Indonesian version of Bidirectional Encoder Representations from Transformers (IndoBERT) model and exploratory data analysis (EDA) augmentation technique to address imbalance dataset issue. The evaluation is conducted by comparing the performance of the IndoBERT model on the dataset and applying EDA techniques to enhance the representation of minority classes. The result of this research shows that IndoBERT achieves 91% accuracy rate in classifying SMS spam. Furthermore, the use of EDA technique results in significant improvement in f1-score, with an average 12% increase in minority classes. Overall model accuracy also improves to 93% after EDA implementation. This research concludes that IndoBERT is effective for multiclass SMS spam classification, and the EDA is beneficial in handling imbalanced data, contributing to the enhancement of model performances.
Implementasi Fuzzy C-Means untuk Pengelompokan Daerah berdasarkan Persebaran Penularan Covid-19 Nugraha, Gibran Satya; Dwiyansaputra, Ramaditia; Bimantoro, Fitri; Aranta, Arik
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 10 No 1: Februari 2023
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2023105796

Abstract

Peningkatan kasus Covid-19 di Indonesia memberikan rasa khawatir bagi hampir seluruh masyarakat, Dilihat dari persebaran tiap provinsi untuk kasus positif, sembuh, dan meninggal tidak menunjukkan sebuah grafik yang linier. Seperti pada data harian kasus per provinsi di akhir bulan April 2021 dimana kasus positif dan sembuh terbanyak terdapat pada Provinsi DKI Jakarta, untuk kasus meninggal Provinsi Jawa Timur berada di posisi pertama, dan di posisi empat untuk kasus positif dan meninggal. Data persebaran yang abstrak ini membuat pengelompokan persebaran Covid-19 di Indonesia menjadi sukar untuk dilakukan. Penelitian ini mengelompokkan provinsi-provinsi berdasarkan persebaran Covid-19 di Indonesia dengan cara mengimplementasikan metode Fuzzy C-means serta metode Elbow. Fuzzy C-means adalah metode pengelompokan berbasis fuzzy yang dapat melakukan persebaran data pada seluruh cluster berdasarkan derajat keanggotaan yang dimilikinya. Sedangkan untuk menentukan jumlah cluster terbaik akan diimplementasikan metode Elbow. Metode Elbow membandingkan perbandingan hasil sum square error (SSE) dari setiap cluster dan mendapatkan jumlah cluster terbaik dari perubahan nilai SSE yang signifikan atau membentuk siku (elbow). Penggunaan Fuzzy c-means sebagai metode pengelompokan untuk mencari tahu seberapa besar pengaruh yang dimiliki setiap data terhadap masing-masing cluster. Karera metode-metode sebelumnya yang digunakan pada objek yang sama hanya melakukan pengelompokan saja secara tegas, tanpa memperhatikan besarnya pengaruh sebuah data terhadap seluruh cluster. Pengelompokan dilakukan menjadi tiga buah cluster atau kelompok berdasarkan parameter kasus positif, sembuh, dan meninggal Covid-19 per 27 April 2021. Cluster 1 hanya terdiri tiga provinsi yaitu Jawa Barat, Jawa Tengah, dan Jawa Timur. Cluster 2 DKI Jakarta, dan sisanya masuk ke cluster 3. AbstractThe increase in Covid-19 cases in Indonesia raises concerns for all parties, When viewed for the distribution of each province, positive, recovered and dead cases do not show a linear graph. As in the daily data of cases per province at the end of April 2021 where the most positive and recovered cases were in DKI Jakarta Province, while for dead cases, East Java Province was in first position, and in fourth position for positive and dead cases. This abstract distribution data makes it difficult to classify the distribution of Covid-19 in Indonesia. This study will group provinces based on the spread of Covid-19 in Indonesia using the Fuzzy C-means method and the Elbow method. Fuzzy C-means is a fuzzy-based grouping method that allows all data to be members of all clusters formed with their respective degrees of membership. Meanwhile, to determine the best number of clusters, the Elbow method will be implemented. The Elbow method compares the sum square error (SSE) results from each cluster and gets the best number of clusters from a significant change in the SSE value or forms an elbow. The use of Fuzzy c-means as a grouping method to find out how much influence each data has on each cluster. Because the previous methods used on the same object only grouped it explicitly, without paying attention to the effect of one data on the entire cluster. The grouping was carried out into three clusters or groups based on the parameters of positive cases, recovered, and died of Covid-19 as of 27 April 2021. Cluster 1 only consisted of three provinces, namely West Java, Central Java, and East Java. Cluster 2 DKI Jakarta, and the rest go to cluster 3. It takes a grouping test to determine how accurate the results are.
CLASSIFICATION OF DENTAL CARIES DISEASE IN TOOTH IMAGES USING A COMPARISON OF EFFICIENTNET-B0, MOBILENETV2, RESNET-50, INCEPTIONV3 ARCHITECTURES Wahyuningsih, Wahyuningsih; Nugraha, Gibran Satya; Dwiyansaputra, Ramaditia
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 4 (2024): JUTIF Volume 5, Number 4, August 2024 - SENIKO
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.4.2187

Abstract

Dental caries is a global metabolic disorder, influenced by complex interactions between the body and microbes, it's caused by prolonged exposure to a low pH environment, leading to demineralized carious lesions. If untreated, it can cause pain and eating difficulties, requiring emergency care and significantly impacting overall quality of life. Diagnosis process can be conducted through physical assessment and analyzing laboratory testing. Image-based artificial intelligence systems, particularly the EfficientNet-B0 model, is suggested as a resolution for classifying dental caries using tooth images. The study's goal is to assess EfficientNet-B0's performance in comparison to other CNN architectures and play a role in advancing medical image classification technology. The original dataset comprising 1554 images was initially collected. After augmentation, the dataset expanded to 6348 images. The data was then divided into three subsets of training, validation, and testing datasets with a distribution ratio of 70:15:15, respectively. From all the evaluated models, the EfficientNet-B0 demonstrated a quite commendable accuracy of 0.98% with overfitting tolerance of less than 2%. Having the same accuracy as the MobileNetV2 (0.98%). Despite its inability to exceed the accuracy achieved by ResNet-50 (0.99%), EfficientNet-B0 accomplished its accuracy level with roughly a quarter of the parameters than ResNet-50 and highger than InceptionV3 (0.97%), highlighting its efficiency in parameter utilization and computational resource management. These findings hold promise for enhancing models and guiding clinical decision-making.
Matlab Program for Sharpening Image due to Lenses Blurring Effect Simulation with Lucy Richardson Deconvolution Muhammad, Fathony Arroisy; Nugraha, Gibran Satya; Dwiyansaputra, Ramaditia
AMPLITUDO : Journal of Science and Technology Innovation Vol. 2 No. 1 (2023): February
Publisher : Balai Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56566/amplitudo.v2i1.57

Abstract

This research was conducted to simulate digital image sharpening using the Lusi Richardson deconvolution method. Sharpening was then performed by Lusi richardson deconvolution of the pint spread function of the lens effect. This point spread function is modeled mathematically with a mathematical function approach. The results of the convolution between the Digital Image from a photo of an object are then convolved with the point spread function so as to produce a blurry image. The blurry image is then re-sharpened by deconvolution using the Lucy Richardson convolution method. The results of this deconvolution are then compared with the image of an object photo of reference and then the difference is calculated. The slight difference between the deconvolution result image and the original object photo image indicates that the program is running well. Peak Signal to Noise Ratio (PSNR) Is used to determine image sharpening recovery. The optimum sharpening recovery of deconvolution iteration is obtained in the maximum PSNR value
Development of Android Based Halal Tourism Application For Travelers Using Scrum Method A.M., Mursyidhan Ariefbillah; Afwani, Royana; Dwiyansaputra, Ramaditia; Saufi, Akhmad
Journal of Information System Research (JOSH) Vol 5 No 4 (2024): Juli 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v5i4.5588

Abstract

Various strategies have been employed to rejuvenate the tourism industry, including enhancing halal tourism services. The halal tourism market segment is highly significant in terms of visitor number and expenditure during travel. This research addresses the technological challenges faced by halal tourism by developing an integrated Android application designed to facilitate hotel reservations, tourist transportation, tour packages, and access to halal-related information. Academics from several fields of University of Mataram play a crucial role in this project, serving as both Product Owners and Scrum Masters, guiding the development team, facilitating discussions, and ensuring the alignment of the application with halal tourism regulations. The application aims to enhance convenience and efficiency for Muslim Travelers by providing easy access to services and information, thus promoting more accessible and effective travel planning. This initiative supports the specific needs of Muslim Travelers and contributes to the economic growth and sustainability of halal tourism in the region. The User Interface (UI) and User Experience (UX) testing using the System Usability Scale (SUS) yielded a score of 82.5 with a grade "A". From the test results, it was concluded that the application, through the exploration of academic insights, is capable of producing an effective solution and meeting the needs of users, thereby improving the quality of services for tourists.
Co-Authors A.M., Mursyidhan Ariefbillah Afwani, Royana Agitha, Nadiyasari Akhmad Saufi Akhyar, Halil Aranta, Arik Ario Yudo Husodo, Ario Yudo Budi Irmawati Darmawan, Muhammad Ilham Darmawan, Riski Dewi, Zaskia Elvina Dwi Ratnasari Ekaputra, Galang Prasetya Fadhilah, A. Nur Fitri Bimantoro Hadi, Risman Hamidi, Mohammad Zaenuddin Hanifah, Fairuz Hidayat, Lalu Ramdoni Hirkan, Muhamad Nurul I Gede Pasek Suta Wijaya Ika Murpratiwi, Santi Ita Selvia, Siska Kokong, Diah Anggreni Ratna Sari Kusuma, Fendi Putra Latifah, Nurun Manuaba, Ida Bagus Ryand Wirayana Maulana, Sutan Fajri Muhammad Dani Muhammad, Fathony Arroisy Nugraha, Gibran Satya Nurfauziyah, Nurfauziyah Nurhasiyah, Nurhasiyah Pahrul Irfan Paramarta, Muhammad Magistra Apta Rahayu, Sefani Cahyo Auliya Rassy, Regania Pasca Rizqullah, Muhammad Naufal Rohmawati, S. Antya Suhada, Destia Susi Rahayu Tiara, Baiq Najwa Tresna, I Made Agus Wahyuningsih Wahyuningsih Widiarta, I Putu Angga Purnama Widiyanti, Astrini