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All Journal JITK (Jurnal Ilmu Pengetahuan dan Komputer) JMM (Jurnal Masyarakat Mandiri) Jurnal Matematika UNAND Jurnal Abdimas Ilmiah Citra Bakti (JAICB) Jurnal Teknik Informatika (JUTIF) JURPIKAT (Jurnal Pengabdian Kepada Masyarakat) Jurnal EduHealth Jurnal Pengabdian Masyarakat : Pemberdayaan, Inovasi dan Perubahan Jurnal Hilirisasi Technology kepada Masyarakat (SITECHMAS) eProceedings of Engineering Journal of Technology and Food Processing (JTFP) Jurnal Ecotipe (Electronic, Control, Telecommunication, Information, and Power Engineering) Folia Medica Indonesiana
Adanti Wido Paramadini
Universitas Telkom
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Journal : Jurnal Teknik Informatika (JUTIF)

 1 
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.
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 Afifah Zen, Nur Afrianto, Arman Ajeng Dyah Kurniawati Alfin Hikmaturokhman An-Naayif, Hanief Taqiyuddien Adz-Dzaky Awaludin, Muhammad Tata Briliana, Carlita Wahyu Dading Qolbu Adi Dasril Aldo Dedy Agung Prabowo Dian Maharani Dyah Kurniawati, Ajeng Faiz, M. Hanif Al Faizah Faizah Faizah Faizah Faizah Fathan, Faizal Burhani Ulil Fau, Andrew Fiqrian, Muhammad Nafal Firmansyah, Muhammad Raafi'u Fitranaya Arlian Fitriawan, Taufiq Gusnita Linda Haibah, Wulan Nawwar Hidayat, Afifah Naurah Ida Saroya, Nikita Ihsan Maulana Ilwan Syafrinal Indah Permatasari Irmayatul Hikmah Isfandiary, Andini Jabbar, Hajria Juan Timotius Prasetya, Yosafat Lina Fatimah Lishobrina M Yoka Fathoni Mahazam Afrad Marsaly, Silvia Van Maulana, Ihsan Miftahul Ilmi Muadin, Dika Alim Muhammad Afrizal Amrustian Muhammad Tata Awaludin Muzakiyah, Lukvinda Asfi Nurul Latifasari Nyimas Ananda Putri Mulyono Oktavia, Laksmi Dwi Pamuji, Yanuar Ikhsan Pratama, Rizal Wahyu Prihartini Widiyanti Putri Anjani, Isabella Putri, Dina Kartika Sa'adah, Aminatus Salsabila, Luciana Santoso, Dimas Sapta Eka Putra Sevia Indah Purnama Silvia Van Marsally Silvia Van Marsally Suci Rahma Sari, Deli Sulaeman, Gilang Sumarsini, Erin Suprapto, Amelia Rut Uzlifatul Arridla, Putri Yasin, Feri Yohani Setiya Rafika Nur Yoka Fathoni, M.