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All Journal Jurnal Teknologi Informasi dan Ilmu Komputer INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi KACANEGARA Jurnal Pengabdian pada Masyarakat International Journal of Engineering, Technology and Natural Sciences (IJETS) Journal of Computer System and Informatics (JoSYC)
Tri Untoro, Iwan Hartadi
Universitas Teknologi Yogyakarta
Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering

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Template Matching Algorithm Implementation For Introduction To Indonesian Traditional House Agus Sujarwadi; Joseph Carlo K; Iwan Hartadi TU; Erik Iman HU; Suhirman; A.Djoko Budiyanto; Suyoto; Natan Derek
Jurnal Internasional Teknik, Teknologi dan Ilmu Pengetahuan Alam Vol 4 No 2 (2022): International Journal of Engineering, Technology and Natural Sciences
Publisher : University of Technology Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (496.472 KB) | DOI: 10.46923/ijets.v4i2.182

Abstract

Digital image processing is an important way in computer vision to determine the shape of digital image objects. Many image processing applications have been produced, one of which is pattern recognition. Pattern recognition can be interpreted as the process of classifying a number of objects into several categories based on similarities and similarities in characteristics. In this study, the pattern recognition of traditional houses in Indonesia will be carried out. In the pattern recognition process, several stages of image processing (image pre-processing) are needed which aims to make the pattern obtained can be recognized accurately. For the introduction of this traditional house, several stages will be carried out before it can finally be recognized by the system. First, the image will be converted into a gray image, after the gray image is obtained, it will be converted into a binary image using thresholding and then followed by normalizing the image size (resize). After that, the resized image will be recognized using the Template Matching method. The results of this study, using 20 examples of traditional house patterns categorized into 10 types of traditional houses in Indonesia, the system can recognize 16 traditional house patterns correctly with an accuracy percentage of 80%. But this system also still has imperfections. Imperfections occur due to several factors such as taking the angle of the object in the test image that does not match the angle of the object in the target image, and also the presence of additional objects that cover the shape of the traditional house that will be recognized; such as: trees, cars, etc., as well as poor test image conditions.
Fuzzy Logic Implementation In Internet Of Things Technology For Foging Greenhouse Plants Suyud Widiono; Iwan Hartadi TU
Jurnal Internasional Teknik, Teknologi dan Ilmu Pengetahuan Alam Vol 5 No 1 (2023): International Journal of Engineering, Technology and Natural Sciences
Publisher : University of Technology Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46923/ijets.v5i1.205

Abstract

Misting of plants in a greenhouse has an important role in maintaining the environmental humidity that plants need. Obstacles faced to maintain the humidity of the plant environment is the use of a thermometer as a measuring tool and misting is still manual. Based on these problems, this research presents an automatic misting system for plants in a greenhouse using the fuzzy logic method. The process of taking data on the temperature and humidity of the plant environment in the greenhouse is from the DHT11 sensor value which is read by the microcontroller to be stored in the Firebase cloud database, the temperature and humidity data in the cloud database is sent and displayed in real-time by an android application built with the MIT tool App Inventor 2. Apart from displaying temperature and humidity data from Firebase, the Android application also processes it using fuzzy logic to produce PWM (Pulse Width Modulation) values to set the fog time. The fuzzy logic method was chosen to map input data problems to output data in conducting fog control. From the sensor test, the results obtained from the comparison of DHT11 sensor readings have an average error of 2.73%. The duration of watering is carried out using Fuzzy logic, the total amount of watering in a day is 2,500 ml of water with a duration of 75 seconds, so that the fogging with the fuzzy logic method is in accordance with the conditions needed in the greenhouse room.
Peningkatan keterampilan komputer bagi Siswa SDN 1 Sinduadi Sleman Ratnawati, Dwi; Tri Untoro, Iwan Hartadi; Vivianti, Vivianti; Hardiyantari, Oktavia; Fatmawati, Soraya; Widodo, Tri; Avianto, Donny
KACANEGARA Jurnal Pengabdian pada Masyarakat Vol 6, No 3 (2023): Agustus
Publisher : Institut Teknologi Dirgantara Adisutjipto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28989/kacanegara.v6i3.1615

Abstract

Perkembangan teknologi informasi dan komunikasi mengusai semua bidang, terutama bidang Pendidikan. Kegiatan ujian berbasis paperless diwajibkan pemerintah dilakukan oleh semua sekolah. Banyak siswa yang belum memiliki kemampuan dalam komputer dasar sehingga mereka kesulitan dalam mengerjakan ujian dalam bentuk paperless. Permasalahan tersebut diselesaikan dengan mengadakan pelatihan Peningkatan Keterampilan Komputer bagi Siswa SD N 1 Sinduadi, Sleman yang dilakukan selama dua minggu. Pelaksanaan ini dilakukan dengan pendampingan dari tim dosen Universitas Teknologi Yogyakarta. Kegiatan ini dilakukan untuk mengatasi kesulitan siswa dalam mengoperasikan komputer dasar. Hasil dari pelaksaan ini adalah  78% siswa mampu meningkatkan kompetensi mengoperasikan komputer dasar dengan baik, dan 22% belum dapat menguasai komputer dasar dengan baik
Klasifikasi Sentimen Opini terhadap Film Kartini Menggunakan Naive Bayes pada Platform X Ramadhani, Muhammad Al-Fajr; Aryanto, Joko; Untoro, Iwan Hartadi Tri; Sujarwadi, Agus
Journal of Computer System and Informatics (JoSYC) Vol 6 No 1 (2024): November 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v6i1.6208

Abstract

Film is an audiovisual media that not only serves as entertainment, but can also provide information and touch the emotions of its audience. In the development of the digital era, people tend to use social media to search for films that are popular or trending. One platform that is often used for this purpose is Twitter, which in July 2023 changed its name to ‘X’. This research discusses the sentiment analysis of the film Kartini on the X social media platform, which tells the story of R.A. Kartini's struggle for the rights of Indonesian women. The film became a topic of conversation with various opinions, both positive and negative, circulating on the platform. The amount of public sentiment often creates confusion for potential viewers in determining whether the film is worth watching. These varying views on the film can affect viewers' perceptions, which in turn risks creating a disappointing viewing experience and making viewers feel that the time spent was not worthwhile. Therefore, the researcher aims to classify the sentiment of platform X users towards the film Kartini using the Naive Bayes Classifier method, which works based on Bayes' Theorem to predict the probability of an event based on previous data. This method was chosen due to its ability to efficiently classify sentiment into positive or negative categories. Temporary results on testing data using the naive bayes algorithm and python programming language obtained very good results. The best accuracy result of the emotion dataset obtained is 98% and the best accuracy of the sentiment dataset obtained is 86%.
Upaya peningkatan kesadaran keamanan data bagi guru Bahasa Inggris SMA di Kabupaten Bantul Rianto, Rianto; Tri Untoro, Iwan Hartadi
KACANEGARA Jurnal Pengabdian pada Masyarakat Vol 7, No 3 (2024): Agustus
Publisher : Institut Teknologi Dirgantara Adisutjipto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28989/kacanegara.v7i3.2106

Abstract

Saat ini, telepon cerdas (smartphone) bukan hanya alat untuk berkomunikasi, tetapi merupakan alat bantu dalam menyelesaikan pekerjaan manusia sehari-hari. Hal ini karena kemajuan teknologi yang berhasil memadukan kecanggihan telekomunikasi dan teknologi informasi dalam satu genggaman. Namun, intensitas penggunaan smartphone yang tinggi ini menimbulkan celah dalam keamanan data dan informasi bagi penggunanya. Pengabdian kepada Masyarakat (PKM) ini bertujuan untuk meningkatkan kesadaran pengguna smartphone dalam keamanan data dan informasi sehingga kejahatan dunia maya dapat diminimalkan. PKM ini termotivasi dari data hasil kuesioner mengenai kesadaran keamanan data dan informasi bagi guru Bahasa Inggris pada Sekolah Menengah Atas (SMA) di kabupaten Bantul. Sebagai tindak lanjut kemudian diadakan Workshop on Digital Literacy: Internet & Mobile Security. Meskipun banyak faktor yang dapat mempengaruhi terjadinya kejahatan dunia maya (cyber-crime), tetapi workshop ini berhasil membekali peserta dengan pengetahuan dasar untuk meminimalkannya.
Enhancing SVM-Based Classification Performance on Indonesian Sentences through TF-IDF and Directional Augmentation Rianto, Rianto; Humanika, Eko Setyo; Untoro, Iwan Hartadi Tri
INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi Vol 10 No 1 (2026)
Publisher : Universitas Nusantara PGRI Kediri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29407/intensif.v10i1.25179

Abstract

Background: The distinction between standard and non-standard Indonesian sentences is traditionally well-defined, yet the ubiquity of digital communication has increasingly blurred these boundaries. This convergence introduces significant lexical ambiguity in formal contexts, complicating the performance of automated text classification systems. Objective: This study aims to enhance the robustness of Support Vector Machine (SVM) classification by addressing these linguistic irregularities through TF-IDF vectorization and a targeted directional augmentation strategy. Methods: A corpus comprising 5,394 labeled sentences was processed under a strict anti-leak grouping strategy to rigorously prevent semantic leakage between training, validation, and testing sets. To resolve decision boundary overlaps often missed by the baseline model, manual directional augmentation was applied, specifically targeting ambiguous sentence structures to enrich the training distribution and linguistic diversity. Results: The experiments demonstrated that directional augmentation significantly refined the model's decision margins. While the baseline model achieved a test accuracy of 94.39%, the augmented approach substantially improved generalization capabilities across unseen groups, elevating validation accuracy from 96.11% to 97.39% and test accuracy to 96.16%. Conclusion: These findings substantiate that structurally enriching the dataset effectively mitigates overfitting and improves sensitivity. However, given the scalability constraints of manual intervention, future research should prioritize automated augmentation techniques and contextual embeddings to handle deep linguistic nuances further.
Peningkatan Sensitivitas Support Vector Machine Pada Klasifikasi Kalimat Baku Dan Tidak Baku Bahasa Indonesia Rianto, Rianto; Humanika, Eko Setyo; Tri Untoro, Iwan Hartadi
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 13 No 3: Juni 2026
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2026133

Abstract

Kalimat dalam bahasa Indonesia dapat diklasifikasikan menjadi dua jenis, yaitu kalimat baku dan tidak baku. Kalimat baku digunakan sebagai bahasa resmi dalam acara formal, sementara kalimat tidak baku umum ditemukan dalam komunikasi sehari-hari. Perkembangan teknologi digital turut mendorong pergeseran penggunaan bahasa, sehingga banyak kalimat tidak baku muncul dalam konteks formal. Penelitian ini bertujuan untuk meningkatkan akurasi model klasifikasi kalimat baku dan tidak baku berbahasa Indonesia. Metode yang digunakan adalah supervised learning dengan Support Vector Machine (SVM) menggunakan dataset berjumlah 2.000 kalimat. Peningkatan akurasi dilakukan melalui TF-IDF Vectorizer dan augmentasi data. Hasil penelitian menunjukkan bahwa sebelum improvisasi, akurasi model mencapai 98.6% dengan total empat kesalahan klasifikasi pada kalimat baku pendek dan ambigu. Setelah improvisasi, akurasi meningkat menjadi 99.3%, dengan jumlah kesalahan total menurun menjadi tiga. Kebaruan penelitian ini terletak pada fokus klasifikasi kalimat baku dan tidak baku dalam konteks bahasa Indonesia, yang masih jarang dieksplorasi. Kontribusinya adalah menyediakan model yang dapat menjadi dasar aplikasi pemeriksa tata bahasa atau sistem penyaringan teks formal. Namun, penelitian ini memiliki keterbatasan pada augmentasi data yang masih dilakukan secara manual. Penelitian selanjutnya diharapkan dapat mengembangkan sistem augmentasi otomatis serta menambah jumlah dataset untuk meningkatkan generalisasi model dan penerapan lebih luas dalam NLP Indonesia.   Abstract Sentences in Indonesian can be classified into two types: standard and non-standard. Standard sentences are used as the official language in formal events, while non-standard sentences are used in daily conversation. The development of digital technology has contributed to the language shift, which causes many non-standard sentences to be used in standard contexts. This study aims to improve the accuracy of the classification model for standard and non-standard sentences in Indonesian. The method used is supervised learning with a Support Vector Machine (SVM) using a dataset of 2,000 sentences consisting of standard and non-standard forms. Accuracy improvement is carried out through TF-IDF Vectorizer and data augmentation. The results showed that the model accuracy reached 98.6% before improvisation, with four misclassifications in short and ambiguous standard sentences. After improvisation, the accuracy increased to 99.3%, with the number of errors decreasing to three. The novelty of this study lies in its focus on addressing sentence classification into standard and non-standard forms in the Indonesian language, which has rarely been explored compared to other NLP tasks. Its practical contribution is to provide a baseline model that can support grammar-checking tools or text filtering systems in digital platforms. However, this study has limitations in data augmentation, which still needs to be done manually. Future research is expected to develop an automatic augmentation system and increase the number of datasets to improve model generalization and broaden its application in Indonesian NLP.
Co-Authors A.Djoko Budiyanto Agus Sujarwadi Donny Avianto Dwi Ratnawati, Dwi Eko Setyo Humanika Erik Iman HU Hardiyantari, Oktavia Joko Aryanto Joseph Carlo K Natan Derek Ramadhani, Muhammad Al-Fajr Rianto Rianto Rianto Rianto Soraya Fatmawati Suhirman Suyoto Suyoto Suyud Widiono Tri Widodo Vivianti