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All Journal Techno.Com: Jurnal Teknologi Informasi TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) EXPERT: Jurnal Manajemen Sistem Informasi dan Teknologi Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) RABIT: Jurnal Teknologi dan Sistem Informasi Univrab Script Journal: Journal of Linguistic and English Teaching Syntax Literate: Jurnal Ilmiah Indonesia JURIKOM (Jurnal Riset Komputer) Infotekmesin Budimas : Jurnal Pengabdian Masyarakat Abdimasku : Jurnal Pengabdian Masyarakat DEDIKASI PKM Walisongo Journal of Information Technology Indonesian Journal of EFL and Linguistics SWAGATI: Journal of Community Service Advance Sustainable Science, Engineering and Technology (ASSET) Green Engineering: Journal of Engineering and Applied Science Jati Emas (Jurnal Aplikasi Teknik dan Pengabdian Masyarakat)
Muljono, -
Department Of Computer Science, Universitas Dian Nuswantoro, Indonesia
Religion Agriculture, Biological Sciences & Forestry Humanities Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mechanical Engineering Public Health Social Sciences Other

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Pergerakan Non-Playable Character dalam Game Stealth Menggunakan Finite State Machine Angga Adetiya; Hanny Haryanto; Erlin Dolphina; Erna Zuni Astuti; Muljono Muljono
EXPERT: Jurnal Manajemen Sistem Informasi dan Teknologi Vol 13, No 1 (2023): June
Publisher : Universitas Bandar Lampung (UBL)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36448/expert.v13i1.2858

Abstract

Movement of Non-Playable Character in Stealth Game using Finite State Machine - Stealth is a game genre that relies on hiding skills and strategizing to complete missions. Challenges in the form of enemy Non-Playable Character (NPC) are one of the vital elements of this genre game. However, NPCs generally only move according to a predetermined line or path of movement, so that their movements can be estimated easily after the player has seen them several times. This causes the movement of NPCs to look monotonous and not challenging. Finite State Machine (FSM) is an artificial intelligence method that can simply model enemy behavior. FSM consists of states and transitions where the state is the current state of the NPC, while the transition is the state of a state. This study models the behavior of enemy NPCs in stealth games using FSM so that it is not easy to predict their movements by adjusting idle time, walking, and moving speed The results of this study indicate that NPC can have nine variations of behavior that can change according to these conditions.
Comparison of Information Gain and Chi-Square Selection Features For Performance Improvement of Naive Bayes Algorithm On Determining Students With No PIP Recipients at SMKN 1 Brebes Magus Sarasnomo; Muljono Muljono; M. Arief Soeleman
Syntax Literate Jurnal Ilmiah Indonesia
Publisher : Syntax Corporation

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (2224.578 KB) | DOI: 10.36418/syntax-literate.v7i4.6661

Abstract

All policies of the Smart Indonesia Program (PIP) through the form of the Smart Indonesia Card (KIP) are issued by the government under the auspices of the Ministry of Education and Culture (Kemendikbud) through the National Team for the Acceleration of Poverty Reduction (TNP2K). Helping to alleviate the poor category of students in order to obtain a proper education, prevent children dropping out of school, and fulfill their school needs are the goals of the program. This assistance can be used by students to meet all school needs such as transportation costs to go to school, the cost of buying school supplies, and school pocket money. This study aims to compare the Information Gain and Chi-Square selection features to improve the performance of the Naive Bayes algorithm in determining poor students who are recipients of the Smart Indonesia Program (PIP) at SMKN 1 Brebes, to determine the accuracy of the Naive Bayes, Information Gain and Chi-Square algorithms. and compare the level of accuracy and determine the attributes that affect the accuracy. At this stage, collecting relevant and useful research data, which is collected in the form of literature and data, and processed as research material. Sources of data used in this study in the form of primary data collection and secondary data. The primary data collection technique used in this study was a questionnaire or questionnaire, while the secondary data obtained in this study was through document files. At this stage, preliminary data processing is carried out, the data used is student data of SMKN 1 Brebes in 2021. The initial data collection obtained was 703 data, but not all records were used because they had to go through several stages of initial data processing (data preparation). The results of the Naive Bayes algorithm accuracy of 90.31% with an AUC of 0.967, after the addition of the Information Gain selection feature the accuracy becomes 90.88% with an AUC value of 0.970. The addition of the Information Gain selection feature can help improve the classification performance of the Naive Bayes algorithm even though the accuracy is not maximized. The accuracy of the Naive Bayes algorithm is 90.31% with an AUC of 0.967, after the addition of the Chi-Square selection feature the accuracy becomes 90.88% with an AUC value of 0.970. The accuracy results are not maximized but the addition of the Chi-Square selection feature can also improve the classification performance of the Naive Bayes algorithm. The accuracy of the Naive Bayes algorithm is 90.31% with an AUC of 0.967, after the addition of the Information Gain selection feature and the Chi-Square selection feature the accuracy becomes 90.88% with an AUC value of 0.970. The results of the same accuracy in the use of the Information Gain and Chi-Square selection features to increase the performance of the Naive Bayes algorithm by 0.57% although the accuracy results are still less than optimal.
Pelatihan Digital Branding dalam Mempromosikan UMKM di Karimunjawa Heni Indrayani; Candra Yudha Satriya; Muhamad Hasan Basori; Muljono Muljono; Dewa Ayu Putri Tesalonika; Al Ghiffari Bintang Saputra; Utari Fatma Dewi
DEDIKASI PKM Vol. 4 No. 3 (2023): DEDIKASI PKM UNPAM
Publisher : Universitas Pamulang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32493/dedikasipkm.v4i3.33280

Abstract

UMKMdiKarimunjawamenjadi bagian penting dalam pengembangan wisata bahari sebagai penguatan perekonomian daerah.Hal ini berdasarkan pada kondisi geografis Karimunjawa yang memiliki potensi hasil laut yang dikenalkan melalui beragam platform media digital. Namun, pemanfaatan media sosial masih belum optimal sehingga diperlukan pelatihan digital branding.  Maka dari itu, tujuan dari kegiatan pengabdian masyarakat adalah untuk memberikan pelatihan digital branding dalam mempromosikan UMKM di Karimunjawa. Pelaksanaan pengabdian masyarakat dengan menggandeng BumDes Sejahtera Bahari sebagai mitra ini diawali dengan melakukan penilaian berupa observasi potensi unggulan produk UMKM, perencanaan pada konten digital tentang produk UMKM, produksi video produk UMKM, hingga aksi dan refleksi berupa evaluasi pemahaman dan kemampuan pelaku UMKM. Berdasarkan kegiatan yang dilakukan diketahui bahwa UMKM dapat memanfaatkan branding digital seperti membuat video storytelling dan video animasi kemudian menyebarluaskan konten di beragam platform media sosial seperti facebook, Instagram, youtube, TikTok. Hal ini dilakukan sebagai strategi untuk meningkatkan penjualan dan memperluas jangkauan pasar. Pelaku usaha lokal di Karimunjawa mendapat pengetahuan tentang optimalisasi media 
Gaussian Based-SMOTE Method for Handling Imbalanced Small Datasets Muhammad Misdram; Edi Noersasongko; Purwanto Purwanto; Muljono Muljono; Fandi Yulian Pamuji
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol 9, No 4 (2023): December
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v9i4.26881

Abstract

The problem of dataset imbalance needs special handling, because it often creates obstacles to the classification process. A very important problem in classification is to overcome a decrease in classification performance. There have been many published researches on the topic of overcoming dataset imbalances, but the results are still unsatisfactory. This is proven by the results of the average accuracy increase which is still not significant. There are several common methods that can be used to deal with dataset imbalances. For example, oversampling, undersampling, Synthetic Minority Oversampling Technique (SMOTE), Borderline-SMOTE, Adasyn, Cluster-SMOTE methods. These methods in testing the results of the classification accuracy average are still relatively low. In this research the selected dataset is a medical dataset which is classified as a small dataset of less than 200 records. The proposed method is Gaussian Based-SMOTE which is expected to work in a normal distribution and can determine excess samples for minority classes. The Gaussian Based-SMOTE method is a contribution of this research and can produce better accuracy than the previous research. The way the Gaussian Based-SMOTE method works is to start by determining the random location of synthesis candidates, determining the Gaussian distribution. The results of these two methods are substituted to produce perfect synthetic values. Generated synthetic values are combined with SMOTE sampling of the majority data from the training data, produce balanced data. The result of the balanced data classification trial from the influence of the Gaussian Based SMOTE result in a significant increase in accuracy values of 3% on average.
Improving the Accuracy of House Price Prediction using Catboost Regression with Random Search Hyperparameter Tuning: A Comparative Analysis Faezal Hartono; Muljono Muljono; Ahmad Fanani
Advance Sustainable Science Engineering and Technology Vol. 6 No. 3 (2024): May - July
Publisher : Science and Technology Research Centre Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v6i3.602

Abstract

Achieving a significant improvement over traditional models, this study presents a novel approach to house price prediction through the integration of Catboost Regression and Random Search Hyperparameter Tuning. By applying these advanced machine learning techniques to the King County Dataset, we conducted a thorough regression analysis and predictive modeling that resulted in a marked increase in accuracy. The baseline model, a conventional linear regression, provided a foundation for comparison, evaluating performance metrics such as R-squared and Mean Squared Error (MSE). The meticulous hyperparameter tuning of the Catboost model yielded a remarkable improvement in predictive accuracy, demonstrating the efficacy of sophisticated data science techniques in real estate and property valuation. The percentage increase in accuracy over the baseline model is explicitly stated in the abstract.
Klasifikasi Berita Televisi Menggunakan Metode K-NN, Naïve Bayes dan SVM Tri Wuryantoro; Muljono Muljono; Pujiono Pujiono
JURIKOM (Jurnal Riset Komputer) Vol 11, No 6 (2024): Desember 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v11i6.8420

Abstract

News through television media is still one of the media that is widely used by the public in obtaining the latest information. The Central Java TVRI Public Broadcasting Institution has a news program called Berita Jawa Tengah which airs every day and  doesn’t have a classification system. This research was carried out in several stages, in the initial stage preprocessing was carried out which included: data collection, cleaning, case folding, tokenizing, normalization, stopword removal, stemming, then continued with word weighting (TF-IDF) and finally applying the K-Nearest Neighbor classification method (K-NN), Naïve Bayes and Support Vector Machine (SVM). The results of the classification carried out show that the K-NN classification method has higher results compared to other methods, namely an Accuracy value of 0.94, Precision 0.92, Recall 0.94 and f1-score 0.93, so it can be concluded that Television news classification using the K-NN method is the method that provides the most accurate results.
Introduction of Computational Thinking Models in the Kurikulum Merdeka Through Scratch Games for Teachers at Gaussian Kamil School Semarang: Pengenalan Model Computational Thinking Pada Kurikulum Merdeka Melalui Scratch Game Untuk Guru Di Gaussian Kamil School Semarang Muljono; Wise Herowati; Novianto Nur Hidayat; Harun Al Azies
JATI EMAS (Jurnal Aplikasi Teknik dan Pengabdian Masyarakat) Vol. 8 No. 1 (2024): Jati Emas (Jurnal Aplikasi Teknik dan Pengabdian Masyarakat)
Publisher : DPD Jatim Perkumpulan Dosen Indonesia Semesta

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Efforts to increase understanding of Computational Thinking (CT) in the context of the Independent Curriculum in Indonesia with a focus on strengthening fundamental educational competencies, are carried out by utilizing the Scratch Game learning method. Gaussian Kamil School (GKS) faces the challenge of integrating CT skills in the Merdeka Curriculum, and teachers at GKS undergo training with Unplugged Programming Activities. The use of the Scratch Game with the Bee Bot Robot and the Activity Map board is an innovative solution. This service tests the development of CT skills through algorithm learning, especially through the Scratch Game. The results demonstrated an engaging learning experience and a better understanding of algorithm concepts. The Scratch Game themes prove the effectiveness of this method, especially for students who are not yet familiar with digital technology. This dedication also provides the foundation for a comprehensive Scratch Game module in teaching the algorithmic aspects of CT skills. The final recommendations include expanding the application of the Scratch Game in the teaching of CT algorithms in schools with limited access to technology, while supporting the development of modules that can be widely adopted to improve students' CT skills.
Utilization of Alkane Hydrocarbons in Waste Management to Support a Green Economy Movement for Students GUSTINA ALFA TRISNAPRADIKA; NOOR AGENG SETIYANTO; SUPRIADI RUSTAD; MULJONO; MUHAMMAD NAUFAL
SWAGATI : Journal of Community Service Vol. 4 No. 1 (2026): March
Publisher : Universitas AMIKOM Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24076/swagati.2026v4i1.2645

Abstract

Household waste management, particularly used cooking oil (waste cooking oil), remains an environmental issue due to low public awareness and limited understanding of sustainable waste management practices. Improper disposal of used cooking oil can lead to environmental pollution and pose health risks. This community service activity aims to improve students’ knowledge and skills in processing used cooking oil into value-added aromatherapy candles, while fostering environmental awareness and an entrepreneurial spirit based on the green economy. The method applied is Community Based Participatory Research (CBPR), conducted in two stages: delivery of theoretical material on the 3R concept (Reduce, Reuse, Recycle) and hands-on practice in processing used cooking oil. The activity was carried out at SMK Negeri 9 Semarang involving 23 students. The results indicate an increase in participants’ understanding, as shown by improvements in scores on 3R education (from 59.5 to 83.3) and used cooking oil processing (from 50 to 85). In addition, students were able to produce aesthetically pleasing aromatherapy candles with economic value potential.
DETEKSI EMOSI PADA TWITTER BERBASIS FASTTEXT: EVALUASI PERFORMA ARSITEKTUR CNN DAN GRU Eka Saraswati; Muljono
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 11 No 1 (2026): Januari
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v11i1.7252

Abstract

Text on social media platforms such as Twitter generates large volumes of data that can be utilized to automatically understand user emotions. However, the characteristics of Twitter text, which are short, unstructured, and dominated by informal language and the use of emojis, pose significant challenges for emotion detection, particularly in Indonesian-language texts. This study aims to develop and evaluate a specific preprocessing pipeline to improve the performance of deep learning–based emotion classification models. The proposed preprocessing pipeline includes social media text cleaning, informal language and slang normalization, removal of irrelevant characters, and emoji-to-text conversion, while word representation is performed using FastText word embedding. An experimental method is employed to compare the performance of three deep learning architectures, namely Convolutional Neural Network (CNN), Gated Recurrent Unit (GRU), and a hybrid CNN–GRU model, in predicting five emotion categories: anger, joy, sadness, fear, and love. The dataset consists of 5,079 tweets, which are divided using a stratified split with an 80:20 ratio between training and testing data. Model performance is evaluated using accuracy, precision, recall, and F1-score metrics. The results show that the CNN with FastText model outperforms the other models, achieving an accuracy of 81.2%, precision of 81.1%, recall of 81.9%, and an F1-score of 81.2%, confirming that a specifically designed preprocessing pipeline plays a crucial role in improving emotion detection accuracy for Indonesian Twitter text.
Gaussian Based-SMOTE Method for Handling Imbalanced Small Datasets Muhammad Misdram; Edi Noersasongko; Purwanto Purwanto; Muljono Muljono; Fandi Yulian Pamuji
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 9 No. 4 (2023): December
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v9i4.26881

Abstract

The problem of dataset imbalance needs special handling, because it often creates obstacles to the classification process. A very important problem in classification is to overcome a decrease in classification performance. There have been many published researches on the topic of overcoming dataset imbalances, but the results are still unsatisfactory. This is proven by the results of the average accuracy increase which is still not significant. There are several common methods that can be used to deal with dataset imbalances. For example, oversampling, undersampling, Synthetic Minority Oversampling Technique (SMOTE), Borderline-SMOTE, Adasyn, Cluster-SMOTE methods. These methods in testing the results of the classification accuracy average are still relatively low. In this research the selected dataset is a medical dataset which is classified as a small dataset of less than 200 records. The proposed method is Gaussian Based-SMOTE which is expected to work in a normal distribution and can determine excess samples for minority classes. The Gaussian Based-SMOTE method is a contribution of this research and can produce better accuracy than the previous research. The way the Gaussian Based-SMOTE method works is to start by determining the random location of synthesis candidates, determining the Gaussian distribution. The results of these two methods are substituted to produce perfect synthetic values. Generated synthetic values are combined with SMOTE sampling of the majority data from the training data, produce balanced data. The result of the balanced data classification trial from the influence of the Gaussian Based SMOTE result in a significant increase in accuracy values of 3% on average.
Co-Authors Abimanyu Cahya Pramudhita Aditya Wahyu Ramadhan Agus Sri Giyanti Ahmad Fanani Akbar Dwi Syahputra Al Ghiffari Bintang Saputra Alfina Latifa Maysara Alvino Dwi Rachman Prabowo Angga Adetiya Anggita Alya Salsabila Anisa Larassati Aripin Azhara Devi Sandi Budi Harjo Cinantya Paramita Deshinta Arrova Dewi Dewa Ayu Putri Tesalonika Dhendra Maruhto Edi Noersasongko Eka Saraswati Erlin Dolphina Erna Zuni Astuti Etika Kartikadarma Faezal Hartono Fahmi Amiq Fandi Yulian Pamuji Farikh Al Zami Filmada Ocky Saputra Gustina Alfa Trisnapradika Hanny Haryanto Harun Al Azies Heni Indrayani Jutono Gondohanindijo Karis W. Magus Sarasnomo Mangatur Rudolf Nababan Margareta Valencia Suci Handayani Margareta Valencia Suci Handayani Moch Arief Soeleman Muhamad Hasan Basori Muhamad Ni'am Syukri Roni Asmi Muhammad Misdram Muhammad Naufal Muslih Muslih Nina Setyaningsih Noor Ageng Setiyanto, Noor Ageng Noorsidi Aizuddin Bin Mat Noor Novianto Nur Hidayat Nurhindarto, Aris Pujiono Pujiono Pujiono Pujiono Purwanto Purwanto R Arief Nugroho Raden Arief Nugroho Restu Poetra Alqurni Ricardus Anggi Pramunendar Robiatul Adawiyah Ruri Suko Basuki Saraswati, Galuh Wilujeng Satriya, Candra Yudha Septian Enggar Sukmana Sunardi Sunardi Sunardi SUPRIADI RUSTAD Syaiful A. Septemuryantoro Tri Wuryantoro Utari Fatma Dewi Valentina Widya Suryaningtyas, Valentina Widya Wildani Eko Nugroho, Wildani Eko Wise Herowati Wulanda Yuniar Permatasari Yo Ceng Giap