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All Journal 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 Jurnal Begawe Teknologi Informasi
Dwiyansaputra, Ramaditia
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SPELLING ERROR CORRECTION IN INDONESIAN USING DAMERAU-LEVENSHTEIN DISTANCE DAN N-GRAM Kokong, Diah Anggreni Ratna Sari; Irmawati, Budi; Dwiyansaputra, Ramaditia
JTIKA (Jurnal Teknik Informatika, Komputer dan Aplikasinya) Vol 6 No 1 (2024): March 2024
Publisher : Program Studi Teknik Informatika, Fakultas Teknik, Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jtika.v6i1.169

Abstract

Writing errors or spelling is a thing that needs to be considered because it can affect the calculations performed by some of the topics on Natural Language Processing that relies on the validity of the input data. Several studies have been conducted to correct writing errors that occur, one of which study by Fahma, A. I., et al using n-gram method and Levenshtein distance produced corrections with the best precision value of 0.97 for insertion type and best recall value by 1 for substitution types. With high accuracy, this study proposes to use the algorithm of development of Levenshtein, namely Damerau-Levenshtein, and n-gram methods. Damerau-Levenshtein has the same operations like insertion, deletion, substitution but with the addition of transposition operations between two characters. Damerau not only distinguishes four edit operations but also states that operations in the developed algorithms, can fit about 80% of all human writing errors. The types of n-grams used are bigram (n = 2) and trigram (n = 3). The testing results obtained in this study for the detection accuracy of the precision and recall ranged from 80%-100%. While correction accuracy testing uses equations proposed by Dahlmier and Ng, among the average accuracy values of precision and recall for all three scenarios, scenario C with a top 10 rating has the highest accuracy value of 96%.
PENGENALAN POLA SUKU KATA AKSARA BIMA DENGAN BARIS TANDA BUNYI MENGGUNAKAN EKSTRAKSI CIRI MOMENT INVARIANT DENGAN METODE ANN Rizqullah, Muhammad Naufal; Dwiyansaputra, Ramaditia; Bimantoro, Fitri
JTIKA (Jurnal Teknik Informatika, Komputer dan Aplikasinya) Vol 6 No 1 (2024): March 2024
Publisher : Program Studi Teknik Informatika, Fakultas Teknik, Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jtika.v6i1.188

Abstract

The Bimanese script is one of the archipelago's cultural heritage that needs to be preserved. The problem arose when some Bimanese people doubted the existence of the Bimanese script. Therefore, it is essential to safeguard the Bimanese script and learn the Bimanese script starting from reading and then understanding the letters. After that, add a line of sound marks to entirely understand the Bimanese script's meaning. This study aims to build an Artificial Neural Network (ANN) model to recognize the Bimanese Script Syllable Pattern with Sound Sign Lines by using Moment Invariant feature extraction. Before doing the training, first, determine the parameters on the ANN using the Tuning Hyperparameter, in the test, using a dataset of 2250 images of the Bimanese script. Based on the results of the tests carried out based on the optimal parameters, the accuracy is 77.59%, precision is 78.44%, recall is 77.61%, and F1-Score is 77.33%. Then for testing using K-Fold cross-validation, the best results were obtained using K = 9 with a ratio of 8:1 where the resulting accuracy was 79.74%. Overall the results of this study are expected to preserve the Bimanese script and are developed more widely.
ANALISIS SENTIMEN MASYARAKAT TERHADAP KEBIJAKAN PENERAPAN PPKM DI MEDIA SOSIAL TWITTER DENGAN MENGGUNAKAN METODE XGBOOST Widiarta, I Putu Angga Purnama; Dwiyansaputra, Ramaditia; Aranta, Arik
JTIKA (Jurnal Teknik Informatika, Komputer dan Aplikasinya) Vol 5 No 2 (2023): September 2023
Publisher : Program Studi Teknik Informatika, Fakultas Teknik, Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jtika.v5i2.342

Abstract

Corona Virus Disease (Covid-19) is a virus that causes respiratory infections in humans. Indonesia is a country that has been infected with this virus, the implementation of restrictions on community activities (PPKM) is implemented by the government as a policy to reduce the spread of Covid-19. Pros and cons arise due to the impact of the policy. Therefore, assessing how public opinion or sentiment is towards this policy is important to do. This study aims to implement the XGBoost algorithm in the sentiment classification process. Sentiment analysis targets public opinion on Twitter, the dataset used is 1958 positive tweets and 3980 negative tweets. At the preprocessing stage, case-folding, stopwords removal, tokenizing, and stemming are carried out. Giving weights to terms uses the Term Frequency-Relevance Frequency method to turn each term into a number. In the final stage, classification is carried out by implementing the XGBoost method with optimal hyperparameter scores. K-fold cross validation is used to evaluate model performance. Based on the evaluation results, the best performance was obtained by a model with a hyperparameter value with an n_estimator of 1000, a learning_rate of 0.1, a max_depth of 6, a subsample of 1, a gamma of 0 and utilizing the stem-ming process in preprocessing with an accuracy value of 85.27%. precision of 86.07%, and recall of 85.23%.
Comparison of CNN’s Architecture GoogleNet, AlexNet, VGG-16, Lenet -5, Resnet-50 in Arabic Handwriting Pattern Recognition Nugraha, Gibran Satya; Darmawan, Muhammad Ilham; Dwiyansaputra, Ramaditia
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 8, No. 2, May 2023
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v8i2.1667

Abstract

The Arabic script is written from right to left and consists of 28 characters, with no capital or lowercase letters. The Arabic script has several orthographic and morphological properties that make handwriting recognition of the Arabic script challenging. In addition, one of the biggest challenges in recognizing Arabic script patterns is the different handwriting styles and characters of each person's writing. The authors propose a study to compare the accuracy of handwriting pattern recognition in Arabic script which has been done previously by comparing five CNN architectures, namely GoogleNet, AlexNet, VGG-16, LeNet-5, and ResNet-50. Considering that previous research has not obtained excellent accuracy. The number of datasets used is 8400 image data and the most optimal comparison of testing and training data is 80:20. Based on the research that has been done, there are several things that the author can conclude. The model is made using 64 filters for each convolution layer because the optimal size is used for 5 architectures, kernel size is 3x3, neurons is 128, dropout weight is 50% to reduce overfitting, learning rate is 0.001, image size is 64x64, the normalization method with the ReLU activation function, and 1-dimensional input image (grayscale), and with a comparison of testing and training data of 80:20. The VGG-16 architectural model is the architecture that gets the highest score, namely 83.99%. This can have good potential to be developed as a medium for learning Arabic script.
ANALISIS SENTIMEN PENGGUNA PLATFORM MEDIA SOSIAL X PADA TOPIK PEMILIHAN PRESIDEN 2024 MENGGUNAKAN PERBANDINGAN MODEL MONOLINGUAL DAN MULTILINGUAL BERT Nurhasiyah, Nurhasiyah; Dwiyansaputra, Ramaditia; Ika Murpratiwi, Santi; Aranta, Arik
JATI (Jurnal Mahasiswa Teknik Informatika) Vol. 9 No. 1 (2025): JATI Vol. 9 No. 1
Publisher : Institut Teknologi Nasional Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36040/jati.v9i1.12430

Abstract

Pemilihan Presiden 2024 di Indonesia merupakan topik penting yang banyak dibahas di media sosial, terutama platform X (sebelumnya Twitter). Media sosial ini menyediakan ruang bagi jutaan pengguna untuk berbagi opini yang dapat diolah menjadi data sentiment. Namun, menganalisis opini dalam jumlah besar membutuhkan model yang tepat. Penelitian ini bertujuan untuk menganalisis sentimen publik terkait topik tersebut menggunakan perbandingan model monolingual (IndoBERT) dan multilingual (mBERT), model digunakan untuk mengklasifikasikan sentiment positif, netral, dan negatif. Penelitian dilakukan dengan metode BERT yang meliputi data crawling, preprocessing, labeling, data spliting, implementation model dan evaluation. Dataset terdiri dari 10.140 tweet yang melalui proses preprocessing berupa cleaning, case folding, dan tokenizing, dan proses pelabelan sentiment (positif, netral, negatif). Hasil penelitian menunjukkan bahwa model IndoBERT memiliki akurasi tertinggi sebesar 84% dengan presisi 75%, recall 80%, dan F1-Score 78%, sedangkan mBERT mencatat akurasi 81%, presisi 69%, recall 78%, dan F1-Score 73%. Model IndoBERT terbukti lebih unggul dalam memahami konteks bahasa Indonesia dibandingkan mBERT,terutama pada sentiment positif dan negatif, itu karena keterbatasan dalam menangkap konteks khusus Bahasa Indonesia. Evaluasi menggunakan confusion matrix untuk mendukung pernyataan ini. Hasil penelitian ini diharapkan dapat membantu pengembangan teknologi pemrosesan bahasa alami (NLP) untuk mendukung pemahaman opini publik, terutama di sektor sosial-politik Indonesia.
ANALISIS SENTIMEN PADA PENGGUNA APLIKASI X TERHADAP PEMILIHAN UMUM PRESIDEN 2024 MENGGUNAKAN METODE CONVOLUTIONAL NEURAL NETWORK (CNN) Nurfauziyah, Nurfauziyah; Dwiyansaputra, Ramaditia; Ika Murpratiwi, Santi; Aranta, Arik
JATI (Jurnal Mahasiswa Teknik Informatika) Vol. 9 No. 1 (2025): JATI Vol. 9 No. 1
Publisher : Institut Teknologi Nasional Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36040/jati.v9i1.12437

Abstract

Pemilihan Umum Presiden 2024 menjadi perhatian besar masyarakat Indonesia, terutama di media sosial seperti Aplikasi X. Media sosial menyediakan data yang kaya akan opini publik, namun analisis sentimen terhadap teks tidak terstruktur menghadapi tantangan, seperti pola bahasa informal dan campuran bahasa. Penelitian ini bertujuan menganalisis sentimen publik terkait Pemilu 2024 menggunakan Convolutional Neural Network (CNN). Dataset terdiri dari 10.250 tweet yang dikumpulkan melalui Twitter API selama Juni–September 2024. Data diproses melalui pembersihan, tokenisasi, stemming, pelabelan sentimen menggunakan VADER, dan pelatihan model CNN. Penelitian ini mencakup enam percobaan, termasuk penghapusan stopwords, penggunaan bobot TF-IDF, dan modifikasi arsitektur CNN. Hasil terbaik diperoleh dari kombinasi CNN dan TF-IDF dengan akurasi 84%, precision 83%, recall 83%, dan F1-score 83%. Distribusi sentimen menunjukkan 45,42% positif, 37,53% negatif, dan 17,04% netral. Penelitian ini mengonfirmasi efektivitas CNN dengan TF-IDF dalam menganalisis sentimen dari teks kompleks di media sosial.
IMPLEMENTASI LENET-5 DAN MOBILENET-V2 UNTUK KLASIFIKASI KEMATANGAN BUAH CABAI BERBASIS COMPUTER VISION Hadi, Risman; Dwiyansaputra, Ramaditia; Irfan, Pahrul
JATI (Jurnal Mahasiswa Teknik Informatika) Vol. 9 No. 1 (2025): JATI Vol. 9 No. 1
Publisher : Institut Teknologi Nasional Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36040/jati.v9i1.12725

Abstract

Tingginya konsumsi cabai rawit menjadikannya komoditas dengan permintaan yang selalu tinggi, sehingga memiliki nilai ekonomi yang signifikan bagi para petani . Terlebih lagi, makanan khas Pulau Lombok seperti pelecing kangkung, ayam taliwang, dan bebalung sangat ditentukan cita rasanya oleh cabai sebagai salah satu bahan utama untuk bumbu. Permintaan cabai yang cenderung tinggi sepanjang tahun tanpa mengenal musim menciptakan tantangan dalam pengelolaan kualitas cabai di pasaran. Identifikasi kualitas buah cabai yang masih dilakukan secara visual oleh petani sering kali menghasilkan kesalahan dalam proses sortir yang dapat merugikan konsumen. Oleh karena itu, diperlukan penerapan teknologi computer vision untuk mengatasi masalah ini. Penelitian ini mengimplementasikan arsitektur CNN yaitu Lenet-5 dan MobileNet-V2. Lenet-5 memiliki kelebihan dalam kesederhanaan struktur dan kebutuhan komputasi yang rendah, sehingga cocok digunakan pada perangkat dengan sumber daya terbatas. MobileNet-V2 memiliki unggul dalam efisiensi parameter dan kinerja yang optimal untuk perangkat mobile atau aplikasi real-time. Hasil penelitian menunjukkan bahwa model arsitektur Lenet-5 menunjukkan performa lebih baik dengan accuracy 99%, precission 1.00, recall 1.00 dan F-1 Score 1.00, sedangkan model arsitektur MobileNet-V2 memiliki accuracy 100%, precission 1.00, recall 1.00 dan F-1 Score 0,95. Hal tersebut menunjukkan potensi computer vision berbasis CNN untuk meningkatkan akurasi dan efisiensi dalam klasifikasi tingkat kematangan buah cabai.
Implementation of Digital Marketing Strategy for UMKM in Keroya Village, East Lombok through Optimization of Promotional Design Using Canva: IMPLEMENTASI STRATEGI DIGITALISASI MARKETING DI UMKM DESA KEROYA LOMBOK TIMUR MELALUI OPTIMALISASI DESAIN PROMOSI MENGGUNAKAN CANVA Akhyar, Halil; Bimantoro, Fitri; Dwiyansaputra, Ramaditia; Hamidi, Mohammad Zaenuddin; Maulana, Sutan Fajri; Rahayu, Susi
Jurnal Begawe Teknologi Informasi (JBegaTI) Vol. 6 No. 1 (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.v6i1.1362

Abstract

Desa Keroya memiliki potensi pengembangan ekonomi karena memiliki banyak pelaku usaha mikro di sektor pertanian dan perkebunan. Upaya yang dilakukan untuk mendukung daya saing pelaku usaha mikro dengan mengadakan kegiatan pelatihan digitalisasi marketing. Kegiatan bertujuan untuk meningkatkan pemahaman dan keterampilan dalam strategi promosi menggunakan canva. Kegiatan ini mencakup pelatihan penggunaan aplikasi canva sebagai alat desain promosi serta pengenalan teknis pemasaran berbasis digital. Metode yang diterapkan dalam pelatihan meliputi pendekatan Participatory Rural Apprasial (PRA), Economic Empowerment Model, Technological Empowerment Model, dan edukatif. Hasil evaluasi menunjukkan bahwa pelatihan ini meningkatkan kesadaran peserta terhadap pentingnya digitalisasi marketing serta kemampuan peserta dalam merancang materi promosi secara mandiri. Namun, tantangan seperti keterbatasan perangkat teknologi dan tingkat literasi digital yang beragam menjadi hambatan. Banyak pelaku usaha yang masih kesulitan memanfaatkan platform digital secara optimal karena masih kurangnya pemahaman. Tanpa dukungan yang tepat, kesenjangan digital ini dapat mempengaruhi daya saing pasar yang semakin kompetitif. Oleh karena itu, diperlukan pendampingan berkelanjutan serta peningkatan akses teknologi guna memastikan keberlanjutan program ini. Kolaborasi antara akademisi, pemerintah desa, dan masyarakat menjadi factor kunci dalam mendukung pemberdayaan UMKM melalui digitalisasi marketing.
PERBAIKAN KESALAHAN KATA MENGGUNAKAN KOMBINASI JARO-WINKLER & JACCARD SIMILARITY Tresna, I Made Agus; Dwiyansaputra, Ramaditia; Akhyar, Halil
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.v7i1.435

Abstract

Word error correction is challenging due to the variety of errors. This research proposes a combination of two similarity algorithms to improve accuracy. The objective is to evaluate how each algorithm responds to different types of spelling errors and to assess the effectiveness of their combined performance. Jaro-Winkler determines initial similarity by assigning more weight to word prefixes, effectively addressing errors due to transposition and character omission. This algorithm excels in scenarios where the beginning of the word is critical to identifying the correct candidate. In contrast, Jaccard similarity filters candidates based on character set similarity, which helps assess the overall composition similarity of the word but does not consider character order. The test results show that Jaro-Winkler is more dominant in providing relevant correction candidates, with higher accuracy in the 1-best (68.94%) and 5-best (90.78%) scenarios compared to Jaccard (55.25% and 78.42%). This performance difference suggests that Jaro-Winkler is more suitable for the initial screening of candidates. The combination of the two algorithms proved to be more effective in handling different types of word errors than when used separately, resulting in a more robust overall correction mechanism.
KLUSTERING TOPIK PADA KOLOM KOMENTAR INSTAGRAM TENTANG KABINET MERAH PUTIH MENGGUNAKAN METODE K-MEANS Rahayu, Sefani Cahyo Auliya; Dwiyansaputra, Ramaditia; Husodo, Ario Yudo
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.v7i1.455

Abstract

This research attempts to determine the primary themes that Indonesians talked on President Prabowo Subianto's "Merah Putih" cabinet by using clustering analysis of Instagram comments. Using crawling data from the Instagram platform with the hashtag #KabinetMerahPutih, comments were gathered using the K-Means Clustering approach. Prior to the data being analyzed to create five clusters, the cleaning and pre-processing procedure, which included tokenization with IndoBERT and dimensionality reduction using Principal Component Analysis (PCA), was able to greatly improve the clustering quality, with the Silhouette Coefficient value rising from 0.010 to 0.200. Out of 23.780 initial data, 9.320 clean data were processed for this investigation. The findings demonstrate that the K-Means algorithm can group comments according to pertinent themes and offer profound understanding of support more responsive public policy analysis.
Co-Authors Akhyar, Halil Aranta, Arik Ario Yudo Husodo, Ario Yudo Budi Irmawati Darmawan, Muhammad Ilham Dewi, Zaskia Elvina Fitri Bimantoro Hadi, Risman Hamidi, Mohammad Zaenuddin 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 Nugraha, Gibran Satya Nurfauziyah, Nurfauziyah Nurhasiyah, Nurhasiyah Pahrul Irfan Paramarta, Muhammad Magistra Apta Rahayu, Sefani Cahyo Auliya Rassy, Regania Pasca Rizqullah, Muhammad Naufal Susi Rahayu Tiara, Baiq Najwa Tresna, I Made Agus Widiarta, I Putu Angga Purnama Widiyanti, Astrini