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All Journal JTEV (Jurnal Teknik Elektro dan Vokasional Jupiter SITEKIN: Jurnal Sains, Teknologi dan Industri Jurnal Teknologi Informasi dan Ilmu Komputer Informatika Mulawarman: Jurnal Ilmiah Ilmu Komputer Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Journal of Information Technology and Computer Science (JOINTECS) JURNAL MEDIA INFORMATIKA BUDIDARMA INOVTEK Polbeng - Seri Informatika JITK (Jurnal Ilmu Pengetahuan dan Komputer) JURNAL TEKNOLOGI DAN OPEN SOURCE Jurnal Sains dan Informatika : Research of Science and Informatic J-SAKTI (Jurnal Sains Komputer dan Informatika) KOMPUTIKA - Jurnal Sistem Komputer JOISIE (Journal Of Information Systems And Informatics Engineering) Jurnal Abdimas PHB : Jurnal Pengabdian Masyarakat Progresif Humanis Brainstorming METIK JURNAL JSAI (Journal Scientific and Applied Informatics) Building of Informatics, Technology and Science Jurnal Mantik JUKANTI (Jurnal Pendidikan Teknologi Informasi) Journal of Applied Engineering and Technological Science (JAETS) JURSIMA (Jurnal Sistem Informasi dan Manajemen) JISA (Jurnal Informatika dan Sains) JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Idealis : Indonesia Journal Information System Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Jurnal Teknik Informatika (JUTIF) J-SAKTI (Jurnal Sains Komputer dan Informatika) Jurnal Esensi Infokom: Jurnal Esensi Sistem Informasi dan Sistem Komputer Jurnal Pengabdian Masyarakat : Pemberdayaan, Inovasi dan Perubahan Kolaborasi Jurnal Pengabdian Masyarakat Jurnal Teknoif Teknik Informatika Institut Teknologi Padang IJCOSIN : Indonesian Journal of Community Service and Innovation Jurnal Masyarakat Madani Indonesia eProceedings of Engineering Jurnal Pengabdian Masyarakat Bangsa JURSIMA SATIN - Sains dan Teknologi Informasi Jurnal Abdimas Indonesia Jurnal Informatika: Jurnal Pengembangan IT Jurnal Sistem Informasi dan Manajemen Jurnal Pengabdian Sosial RESLAJ: Religion Education Social Laa Roiba Journal Jurnal Pendidikan Teknologi Informasi (JUKANTI) Jurnal Ecotipe (Electronic, Control, Telecommunication, Information, and Power Engineering) Jnanadharma KOMPUTASI
Dasril Aldo
Telkom University
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Mechanical Engineering Medicine & Pharmacology Nursing Physics Public Health Social Sciences Other

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Journal : Jurnal Teknik Informatika (JUTIF)

 1 
EXPERT SYSTEM FOR INITIAL IDENTIFICATION OF DISEASES CAUSED BY HELICOBACTER PYLORI BACTERIA USING CASE BASED REASONING APPROACH Dasril Aldo
Jurnal Teknik Informatika (Jutif) Vol. 4 No. 1 (2023): JUTIF Volume 4, Number 1, February 2023
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2023.4.1.693

Abstract

Helicobacter pylori, which is a bacterium that can live in the stomach. Infection can occur when bacteria invade and damage the stomach wall. Lack of information and ignorance of the public about the seriousness of these bacteria causes various very serious diseases such as inflammation of the digestive tract (gastritis), gastric bleeding, gastric perforation (leak stomach), infection of the peritoneal wall (peritonitis) and gastric cancer. This expert system aims to provide information and also early identification of diseases caused by the bacterium Helicobacter pylori. After the expert system has identified the type of disease, it will then suggest the actions that need to be taken. The method used is CBR, this method works with the stages of Retrieve, Reuse, Revise, and Retain. The data that will be processed in this study are 30 data, with the results of 29 data in accordance with the doctor's diagnosis. From these results, it can be said that the accuracy of this expert system is 97% so that it can be used as an alternative in identifying diseases caused by the bacterium Helicobacter pylori.
TEXT CLASSIFICATION OF BULLYING REPORTS USING NLP AND RANDOM FOREST. Aldo, Dasril; Paramadini, Adanti Wido; Fathoni, M. Yoka
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 1 (2025): JUTIF Volume 6, Number 1, February 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.1.4032

Abstract

Bullying is a great concern that needs to be dealt with as early as possible, be it in the form of physical, verbal, social or cyber bullying. Using NLP algorithms, this paper intends to classify bullying report using Natural Language Processing in conjunction with Bag of Words. The study employs quantitative methodology. A total of 4671 reports of bullying are in essence categorized into physical, verbal, social, cyber and non-cyber bullying. We split the dataset into 80% training set (3737 reports) and 20% testing set (934 reports). The above model has achieved an accuracy of 94,76%, with good values of recall, precision and F1-score: 94,64%, 95,02% and 94,97% respectively. The dataset is then analyzed using Random Forest algorithm and Report of the Bullying Survey The model is to be effective in automatic Detection of Textual Bullying Reports Automated. While there has been no such effort in our institutions so far, automatic reporting of bullying will prove to be effective. This is because the system will allow a school or institution to have a precise constant monitoring of bullying reports. It will also allow an instantaneous action to be taken to protect the victim without letting the situation escalate.
Multivariate Forecasting of Paddy Production: A Comparative Study of Machine Learning Models Yasin, Feri; Firmansyah, Muhammad Raafi'u; Aldo, Dasril; Amrustian, Muhammad Afrizal
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 3 (2025): JUTIF Volume 6, Number 3, Juni 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.3.4681

Abstract

Accurate rice production forecasting plays an important role in supporting national food security planning. This study aims to evaluate the performance of four machine learning algorithms, namely Random Forest, XGBoost, Support Vector Regression (SVR), and Linear Regression, in predicting three target variables simultaneously: harvest area, productivity, and production. The dataset used includes annual data per province in Indonesia from 2018 to 2024 obtained from the Central Statistics Agency (BPS). Evaluation was conducted using five metrics: MAE, RMSE, MAPE, R², and training time. The results of the experiment showed that the Random Forest Regressor performed best in the 80:20 scenario, with an MAE of 76,259.52, an RMSE of 154,036.91, a MAPE of 0.61%, and an R² of 0.997. XGBoost showed a competitive performance with an MAE of 79,381.44 and faster training times. In contrast, the SVR showed the worst performance with the MAPE reaching 198.56% and the R² of 0.209. Linear Regression as baseline recorded an MAE of 1,194,355.28 and an R² of 0.503, indicating that the linear model is not effective enough for this data. The 80:20 scenario is considered the best configuration because it is able to balance the accuracy and generalization of the model. These findings show that the use of ensemble algorithms, especially Random Forest and XGBoost, has the potential to be applied practically by agricultural agencies or local governments in designing data-driven policies for more proactive and predictive rice production management. Furthermore, this study contributes to the advancement of applied informatics by demonstrating how machine learning models can be effectively used in multivariate forecasting for complex, real-world problems, thereby supporting the development of intelligent decision-support systems in the agricultural domain.
Optimization Of Extreme Learning Machine Models Using Metaheuristic Approaches For Diabetes Classification Sulaeman, Gilang; Nur, Yohani Setiya Rafika Nur; Paramadini, Adanti Wido; Aldo, Dasril; Fathoni, M. Yoka
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 3 (2025): JUTIF Volume 6, Number 3, Juni 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.3.4690

Abstract

Proper classification of diabetes is a significant challenge in contemporary healthcare, especially related to early detection and clinical decision support systems. This study aims to optimize the Extreme Learning Machine (ELM) model with a metaheuristic approach to improve performance in diabetes classification. The data used was an open dataset containing the patient's medical attributes, such as age, gender, smoking status, body mass index, blood glucose level, and HbA1c. The initial process includes data cleansing, one-hot coding for categorical features, MinMax normalization, and unbalanced data handling with SMOTE. The ELM model was tested with four activation functions (Sigmoid, ReLU, Tanh, and RBF) each combined with three metaheuristic optimization strategies, namely Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Bat Algorithm. The results of the evaluation showed that the combination of the Tanh activation function with GA optimization obtained the highest accuracy of 87.98% and an F1-score of 0.5489. Overall, GA optimization appears to be superior to all other measurement configurations in consistent classification performance. The main contribution of this study is to offer a systematic approach to select the best combination of activation functions and optimization algorithms in ELM, as well as to provide empirical evidence to support the application of metaheuristic strategies to improve the accuracy of disease classification based on health data. This research has direct implications for the development of a more precise and data-based medical diagnostic classification system for diabetes.
EXPERT SYSTEM WITH DEMPSTER-SHAFER METHOD FOR EARLY IDENTIFICATION OF DISEASES DUE TO COMPLICATIONS SYSTEMIC INFLAMMATORY RESPONSE SYNDROME Wido Paramadini, Adanti; Dasril Aldo; Yoka Fathoni, M.; Yohani Setiya Rafika Nur; Dading Qolbu Adi
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 3 (2024): JUTIF Volume 5, Number 3, June 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.3.2021

Abstract

Systemic Inflammatory Response Syndrome (SIRS) is a generalized inflammatory condition, triggered by various factors such as infection or trauma, which can lead to serious complications if not treated quickly. This condition is characterized by symptoms such as fever or hypothermia, tachycardia, tachypnea, and changes in white blood cell count. Complications that can arise from SIRS include Acute Respiratory Distress Syndrome (ARDS), which results in fluid in the alveoli and requires mechanical ventilation; acute encephalopathy, which leads to brain dysfunction; Asidosis Metabolik, indicating liver damage; hemolysis, which results in the breakdown of red blood cells; and Deep Vein Thrombosis (DVT), which is at risk of causing pulmonary embolism. To overcome this diagnostic challenge, this study implements the Dempster-Shafer method in an expert system, where it allows the aggregation and combination of various sources of evidence to produce degrees of belief and degrees of plausibility for each diagnostic hypothesis. By accounting for uncertainties and contradictions in the data, the system improves diagnostic accuracy through dynamically weighting and updating beliefs based on available evidence. This process allows early and accurate identification of SIRS complications, supporting appropriate medical intervention. System evaluation showed diagnostic accuracy of 93%, confirming the potential of expert systems in supporting rapid and precise clinical decision-making in managing SIRS complications.
Performance Comparison of LSTM Models with Various Optimizers and Activation Functions for Garlic Bulb Price Prediction Using Deep Learning Aldo, Dasril; Paramadini, Adanti Wido; Amrustian, Muhammad Afrizal
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 2 (2025): JUTIF Volume 6, Number 2, April 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.2.4412

Abstract

Accurate commodity price forecasting is crucial for market stability and decision-making. This study evaluates the performance of the Long Short-Term Memory (LSTM) model using various activation functions and optimization algorithms for predicting garlic bulb prices. Historical price data was collected from panelharga.badanpangan.go.id and preprocessed through normalization and dataset splitting into training, validation, and test sets. The model was trained for 200 epochs using activation functions ReLU, Sigmoid, and Tanh, combined with optimization algorithms Adam, RMSprop, SGD, Adagrad, Adadelta, Nadam, and AdamW. Experimental results indicate that ReLU + Adam achieves the best performance with Final Epoch Loss of 0.001789, RMSE of 0.701632, MAPE of 0.009593, and R² of 0.909794, followed by Sigmoid + Nadam and Tanh + Adam, which also yielded high accuracy. These findings reinforce prior research, highlighting Adam and its momentum-based variants as effective optimizers for LSTM training. This study provides insights into selecting optimal activation functions and optimizers for commodity price forecasting. Future work may explore hybrid models and external factors, such as global market trends, to enhance predictive accuracy in time series data analysis.
Co-Authors A.A. Ketut Agung Cahyawan W Abdillah, Alifia Dhia Abimanyu Abimanyu Achmad Solichin Adanti Wido Paramadini Adhe Nuzula Ramadlana Adriano, Riftian Dimas Affiyanti, Rakhma Yuli Afif Dwi Laksono Agung Irman Syaifudin Agustianto, Satya Helfi Ahmad Faishal Fahrisena Ahmad Riau Ardi Ahmad Rijal Arifin Ahmadi Ahun Ismi Aziz Ajeng Ayu Suryani Ajeng Dyah Kurniawati Aksaningtyas, Laeli Lutfiana Al 'Arifah, Difla Mazidah Al Faiz, M. Hanif Alfan Rizki Juliano Azitya Alifia Dhia Abdillah Alika, Shintia Dwi Alwendi, Alwendi Alzi Mula Baharsyah Amanah, Farah Sofiatul Nur Aminatus Sa’adah An-Naayif, Hanief Taqiyuddien Adz-Dzaky Andi Sano, Andreas Novito Andika Bayu S Andre Citro Febriliyan Lanyak Annisa Risqi Sulistya Kusuma Wardhani Apri, Muhamad Ar rasyid, Fauzan Cholis Ardanu, Riski Fitria Ardi - Ardi Ardi Ardi Ardi Ardi, Ahmad Riau Ardianto, Rian Arifin, Ahmad Rijal Army, Widya Lelisa Arrasyid, Zidhan Asti Herliana, Asti Auliya Burhanuddin Aziz, Ahun Ismi Bagus Ahmad Setiawan Baharsyah, Alzi Mula Bidayatul Masulah Bita Parga Zen Briliana, Carlita Wahyu Chevin, Virginawan Alessandro Dading Qolbu Adi Dading Qolbu Adi Damiana Trivinita L. B Dana Eko Wahyu Pambudi Danny Kurnianto Darmansah Darmansah, Darmansah Dedi Rahman Habibie Dedi Rahman Habibie Dedy Agung Prabowo Dedy Mirwansyah Deni Prasetyo Deni Prasetyo, Deni Denis Oktawandira Dewi Larasae Diah Ayu Lestari Diah Ayu Lestari, Diah Ayu Dian Maharani Dian Maharani Dian Riliyanda Difla Mazidah Al 'Arifah Dika Alim Mu’adin Dwi Satrio, Imam Edwin Adhi Wijaya Eko Wahyu Pambudi, Dana Elizabeth Christina Endro Muhammad Akbar Wijiantoro Fadhilatus Salamah, Khanif Rahmah Fahrezy, Fiqry Fahmy Dwe Fahrisena, Ahmad Faishal Fahrullah Fahrullah Faiz, M. Hanif Al Faizah Faizah Fajar Maulana . Farah Sofiatul Nur Amanah Farhan Aryo Pangestu Farhan Rasyid Kamaludin Farhan Yudha Pratama Fathan, Faizal Burhani Ulil Fau, Andrew Fauzi Ahmad Muda Febriliana, Miranda Dwi Feri Yasi Filfimo Yulfiz Ahsanul Hulqi Fiqrian, Muhammad Nafal Fiqry Fahmy Dwe Fahrezy Firmansyah, Muhammad Raafi'u Fuady, Tb. Dedy Gigih Attayauban Purnomo Gusla Nengsih, Yeyi Gustiwa, Risang Abdurrahman Hakim, Faiq Mufrih Halim Pratama, Muhammad Fajrul Hammam, Nizar Dhafirul Hanugrah Surya Purwaka Hariselmi Hariselmi Hariselmi Hariselmi Hariselmi, Hariselmi Hasby Arrahman Hermawaty Hermawaty Hidayat, Afifah Naurah Hutama, Iqbal Yoga Ihsan Maulana Ilwan Syafrinal Iqbal Yoga Hutama Irfan Venny Rahmayanti Irfanza Fadhly, Rafy Islam, Melinta Nurul Ismail Nur Fuadi Jaka Lintang Ramadhan Kamaludin, Farhan Rasyid Khairunnisa Samosir Khanif Rahmah Fadhilatus Salamah Kisviantari, Rizkyna Sekar Kristina Natasia Sinurat Kurniawan, Adrian Kurniawat, Ajeng Dyah Larasae, Dewi Lathif Luqmanul Hakim Lina Fatimah Lishobrina Linda Qornaeni Luqman Wahyudi M Yoka Fathoni M. Aldi Yudhi Pradana Mahazam Afrad Maryona Septiara Maulana Faridzal Eka Nugraha Maulana, Fajar Maulana, Ihsan Maulana, ⁠Ihsan Maulida, Elsa Melinda Br Ginting Melinta Nurul Islam Miftahul Ilmi Miftahul Ilmi, Miftahul Miranda Dwi Febriliana Muadin, Dika Alim Muhamad Apri Muhamad Apri Muhamad Azrino Gustalika Muhammad Afrizal Amrustian Muhammad Agus Muljanto Muhammad Briliantama Putra Muhammad Husni munir, Zainul Munir Nafidanisa Nanda Arista Rizki Nariza Wanti Wulan Sari Nia Annisa Ferani Tanjung Nicolaus Nizar Dhafirul Hammam Novanda Alim Setya Nugraha Nugraha, Alfa Yudha Nugraha, Maulana Faridzal Eka Nursaka Putra NURUL HIKMAH Nurul Hikmah Nyimas Ananda Putri Mulyono Oktavia, Laksmi Dwi Oktawandira, Denis P , Affriza Brilyan Relo Pambudi Agus Pambudi, Dana Eko Wahyu Pamuji, Yanuar Ikhsan Pangestu, Farhan Aryo Pradana, M. Aldi Yudhi Prakoso, Thorik Agung Pratama, Farhan Yudha Purnomo, Gigih Attayauban Putra, Muhammad Briliantama Putri, Yuliarni Rahayu, Trisna Kenti Rakhma Yuli Affiyanti Ramadhan, Firman Adi Ramadhan, Jaka Lintang Ramadhani, Rima Dias Ramadlana, Adhe Nuzula Rania Nur Hikmah Raspati, Mochamad Ravy Ratna Budiarti Dwi Rahayu Reza Iqbal Pramudya Rian Ardianto Rian Ardianto Richki Hardi Richo Richo Rifa Yanti Risfendra, Risfendra Riski Fitria Ardanu Riswan Azhari Riyani, Annisa Defitriana Rizkyna Sekar Kisviantari Rostina Rostina Rostina Rostina Sa'adah, Aminatus Sahara Sahara Salsabila, Luciana Sandhy Fernandez Sapta Eka Putra Saputra , Wahyu Andi Saputra, Candra Eka Saputra, Sandra Saputri, Sekar Isnaeni Nurul Saragih, Lorance Saraswati, RR Michelle Dewi Sarwenty, Putri Nabilah Satya Nur Hutama Sekar Isnaeni Nurul Saputri Setiawan, Bagus Ahmad Setyawan Suroso Sinurat, Kristina Natasia Sophia Deo Sandeva Sri Mulyani Sudianto, Sudianto Sulaeman, Gilang Suleman, Gilang Suprapto, Amelia Rut Suroso, Setyawan Susi Irwanti Susie Susie Syahputra, Dio Syaifudin, Agung Irman Tb. Dedy Fuady Tegar Alamsyah Tohari, Mohammad Amin Tondang, Beny Alphon Toni Anwar Trihastuti Yuniati Trisna Kenti Rahayu Usman, Muhammad Lulu Latif Utami, Annisaa Wanda Ilham Wanda Ilham Wardhani, Annisa Risqi Sulistya Kusuma Wendra, Yumai Widya Lelisa Army Wijaya, Edwin Adhi Wijaya, Trisno Wijiantoro, Endro Muhammad Akbar Yasin, Feri Yoga Madhasatya, Satriya Yogo Dwi Prasetyo Yohani Setiya Rafika Nur Yoka Fathoni, M. Yumai Wendra Yunita, Salsabila Firda Zefanya Yuni Br, Syaloom