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All Journal Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Jurnal Ilmu Komputer dan Agri-Informatika Biogenesis: Jurnal Ilmiah Biologi Journal of Information Systems Engineering and Business Intelligence Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Journal of Electronics, Electromedical Engineering, and Medical Informatics
Medria Kusuma Dewi Hardhienata
Department Of Computer Science, Faculty Of Mathematic And Natural Science, IPB University, Bogor, West Java, Indonesia
Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Immunology & microbiology

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Performance Comparison of Data Sampling Techniques to Handle Imbalanced Class on Prediction of Compound-Protein Interaction Akhmad Rezki Purnajaya; Wisnu Ananta Kusuma; Medria Kusuma Dewi Hardhienata
Biogenesis: Jurnal Ilmiah Biologi Vol 8 No 1 (2020)
Publisher : Department of Biology, Faculty of Sci and Tech, Universitas Islam Negeri Alauddin Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24252/bio.v8i1.12002

Abstract

The prediction of Compound-Protein Interactions (CPI) is an essential step in the drug-target analysis for developing new drugs as well as for drug repositioning. One challenging issue in this field is that commonly there are more numbers of non-interacting compound-protein pairs than interacting pairs. This problem causes bias, which may degrade the prediction of CPI. Besides, currently, there is not much research on CPI prediction that compares data sampling techniques to handle the class imbalance problem. To address this issue, we compare four data sampling techniques, namely Random Under-sampling (RUS), Combination of Over-Under-sampling (COUS), Synthetic Minority Over-sampling Technique (SMOTE), and Tomek Link (T-Link). The benchmark CPI data: Nuclear Receptor and G-Protein Coupled Receptor (GPCR) are used to test these techniques. Area Under Curve (AUC) applied to evaluate the CPI prediction performance of each technique. Results show that the AUC values for RUS, COUS, SMOTE, and T-Link are 0.75, 0.77, 0.85 and 0.79 respectively on Nuclear Receptor data and 0.70, 0.85, 0.91 and 0.72 respectively on GPCR data. These results indicate that SMOTE has the highest AUC values. Furthermore, we found that the SMOTE technique is more capable of handling class imbalance problems on CPI prediction compared to the remaining three other techniques.
Kendali Robot Beroda Otonom dengan Inverse Kinematics Michael Julyus Christopher Manullang; Medria Kusuma Dewi Hardhienata; Karlisa Priandana
Jurnal Ilmu Komputer & Agri-Informatika Vol. 7 No. 1 (2020)
Publisher : Departemen Ilmu Komputer - IPB University

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1049.569 KB) | DOI: 10.29244/jika.7.1.62-73

Abstract

Penelitian ini dilakukan untuk mengembangkan robot otonom yang dikendalikan dengan pendekatan matematika inverse kinematics. Robot yang digunakan pada penelitian ini adalah robot beroda nonholonomic differential drive. Metode penelitian yang digunakan terdiri atas empat tahapan, yaitu: penentuan parameter robot beroda, pengembangan kontrol inverse kinematics, pembuatan data lintasan (trajectory) berupa garis lurus, dan pengujian sistem kendali. Pada pengujian, data trajectory yang dibangkitkan dibandingkan dengan pengukuran berdasarkan observasi di lapangan. Pengukuran data gerakan robot di lapangan dilakukan dengan dua alat, yaitu dengan global positioning system (GPS) yang terpasang pada robot dan GPS smartphone. Hasil pengujian menunjukkan bahwa robot beroda dapat dikendalikan dengan inverse kinematics dengan rata-rata nilai galat sebesar 0.9 meter. Kata Kunci: inverse kinematics, kendali, otonom, robot beroda.
Pembangunan Model Jaringan Saraf Tiruan untuk Memprediksi Kecenderungan Tipe Mediasi Orang Tua terhadap Penggunaan Internet oleh Anak Indah Puspita; Karlisa Priandana; Medria Kusuma Dewi Hardhienata; Peter John Morley; Auzi Asfarian; Husin Alatas
Jurnal Ilmu Komputer dan Agri-Informatika Vol 9 No 1 (2022)
Publisher : Departemen Ilmu Komputer, Institut Pertanian Bogor

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/jika.9.1.47-57

Abstract

Mediasi orang tua sangat diperlukan agar dampak negatif penggunaan internet oleh anak yang tinggi di masa pandemi Covid-19 dapat diminimalisir. Penelitian ini dilakukan dengan membuat model jaringan saraf tiruan (JST) untuk mengetahui hubungan antara faktor dalam keluarga dan teknik mediasi orang tua di wilayah Bogor. JST penelitian ini dibangun menggunakan metode pembelajaran propagasi balik (backpropagation). Faktor dalam keluarga yang diteliti sebagai masukan JST adalah usia orang tua, pendidikan, jumlah anak, usia anak, durasi menggunakan internet, serta jumlah media sosial yang digunakan. Jenis mediasi orang tua yang digunakan sebagai luaran jaringan adalah mediasi aktif penggunaan internet umum, mediasi aktif penggunaan bersama, mediasi pasif penggunaan bersama, mediasi pembatasan aktivitas berinternet, mediasi pembatasan penggunaan internet secara umum, mediasi aktif keamanan internet, mediasi pemantauan, dan mediasi teknis penggunaan internet. Data diperoleh melalui survei terhadap 282 orang tua di wilayah Bogor pada Februari-Juni 2021. Penelitian ini telah membangun model JST untuk memprediksi kecenderungan tipe mediasi orang tua dengan mean-squared error sebesar 0.05132. Model yang dihasilkan dapat dikembangkan lebih lanjut menjadi aplikasi edukasi sederhana yang dapat digunakan oleh orang tua untuk mengetahui jenis mediasi yang mereka lakukan. Dengan lebih memahami jenis mediasi yang mereka lakukan, kami berharap orang tua dapat memiliki pemahaman lebih baik mengenai mediasi orang tua dan dapat menerapkan teknik mediasi yang paling sesuai dengan kondisi yang mereka alami untuk mewujudkan ketahanan keluarga.
Analisis Sentimen Pengguna Twitter Terhadap Program Vaksinasi Covid-19 di Indonesia Menggunakan Algoritme Support Vector Machine Qarry Atul Chairunnisa; Yeni Herdiyeni; Medria Kusuma Dewi Hardhienata; Julio Adisantoso
Jurnal Ilmu Komputer dan Agri-Informatika Vol 9 No 1 (2022)
Publisher : Departemen Ilmu Komputer, Institut Pertanian Bogor

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/jika.9.1.79-89

Abstract

The COVID-19 vaccination policy in Indonesia turns out to be both pros and cons. The government has to evaluate the underlying reason of why some people are against the policy, so that the vaccination program can run smoothly. Sentiment analysis as a way to see the polarity of opinion, makes it possible to classify positive, negative or neutral responses on Twitter regarding the vaccination policy. This study aims to determine the public's response to COVID-19 vaccination in Indonesia by examining word distribution and creating a Support Vector Machine (SVM) classification model. Sentiment analysis consists of several stages, namely data collection, data preprocessing, data weighting, data analysis, data sharing, classification modeling, hyperparameter tuning and model evaluation. The results of this study are a model with a relatively optimal performance in classifying sentiment with an accuracy, precision, recall and f1-score of 90%. The results of the sentiment analysis obtained are in the form of ideas, complaints, and suggestions for the COVID-19 vaccination.
Analisis Sentimen Pengguna Twitter terhadap Vaksinasi COVID-19 di Indonesia menggunakan Algoritme Random Forest dan BERT Amin Elhan; Medria Kusuma Dewi Hardhienata; Yeni Herdiyeni; Sony Hartono Wijaya; Julio Adisantoso
Jurnal Ilmu Komputer dan Agri-Informatika Vol 9 No 2 (2022)
Publisher : Departemen Ilmu Komputer, Institut Pertanian Bogor

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/jika.9.2.199-211

Abstract

The Covid-19 pandemic has encouraged many stakeholders to be able to adapt to current conditions. One of the programs launched by the government in order to overcome the spread of Covid-19 is to run a vaccination program. In order to find out the public's interest in the Covid-19 vaccination program that was launched, it is necessary to carry out a sentiment analysis. Sentiment analysis is generally done to obtain the latest information from a large corpus. The purpose of this study is to analyze the sentiments of Twitter users towards the Covid-19 vaccination in Indonesia using the Random Forest and BERT Algorithms. The research stages include pre-processing Twitter data related to Covid-19 vaccination topics, sentiment labeling, handling unbalanced data, classifying datasets using the Random Forest and BERT algorithms, as well as analysis and evaluation. After handling unbalanced data, the results of Twitter user sentiment analysis for Covid-19 vaccination in Indonesia yielded an accuracy of 81%, F1-score of 74%, precision of 76%, and recall of 74% using the Random Forest algorithm and an accuracy of 82%, F1-score 79%, precision of 78%, and recall of 79% using the BERT Algorithm. Although the BERT Algorithm has generally a slightly higher performance than the Random Forest Algorithm, the simulation results show that the Random Forest algorithm has significantly lower computation time compared to the BERT algorithm in the considered case.
Ant Colony Optimization Modelling for Task Allocation in Multi-Agent System for Multi-Target Iis Rodiah; Medria Kusuma Dewi Hardhienata; Agus Buono; Karlisa Priandana
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 6 No 6 (2022): Desember 2022
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v6i6.4201

Abstract

Task allocation in multi-agent system can be defined as a problem of allocating a number of agents to the task. One of the problems in task allocation is to optimize the allocation of heterogeneous agents when there are multiple tasks which require several capabilities. To solve that problem, this research aims to modify the Ant Colony Optimization (ACO) algorithm so that the algorithm can be employed for solving task allocation problems with multiple tasks. In this research, we optimize the performance of the algorithm by minimizing the task completion cost as well as the number of overlapping agents. We also maximize the overall system capabilities in order to increase efficiency. Simulation results show that the modified ACO algorithm has significantly decreased overall task completion cost as well as the overlapping agents factor compared to the benchmark algorithm.
The Impact of Socioeconomic and Demographic Factors on COVID-19 Forecasting Model Siti Nur Hasanah; Yeni Herdiyeni; Medria Kusuma Dewi Hardhienata
Journal of Information Systems Engineering and Business Intelligence Vol. 9 No. 1 (2023): April
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.9.1.70-83

Abstract

Background: COVID-19 has become a primary public health issue in various countries across the world. The main difficulty in managing outbreaks of infectious diseases is due to the difference in geographical, demographic, economic inequalities and people's behavior in each region. The spread of disease acts like a series of diverse regional outbreaks; each part has its disease transmission pattern. Objective: This study aims to assess the association of socioeconomic and demographic factors to COVID-19 cases through cluster analysis and forecast the daily cases of COVID-19 in each cluster using a predictive modeling technique. Methods: This study applies a hierarchical clustering approach to group regencies and cities based on their socioeconomic and demographic similarities. After that, a time-series forecasting model, Facebook Prophet, is developed in each cluster to assess the transmissibility risk of COVID-19 over a short period of time. Results: A high incidence of COVID-19 was found in clusters with better socioeconomic conditions and densely populated. The Prophet model forecasted the daily cases of COVID-19 in each cluster, with Mean Absolute Percentage Error (MAPE) of 0.0869; 0.1513; and 0.1040, respectively, for cluster 1, cluster 2, and cluster 3. Conclusion: Socioeconomic and demographic factors were associated with different COVID-19 waves in a region. From the study, we found that considering socioeconomic and demographic factors to forecast COVID-19 cases played a crucial role in determining the risk in that area.   Keywords: COVID-19, Facebook Prophet , Hierarchical clustering, Socioeconomic and demographic
Network-Based Molecular Features Selection to Predict the Drug Synergy in Cancer Cells Syarifah Aini; Wisnu Ananta Kusuma; Medria Kusuma Dewi Hardhienata; Mushthofa
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 5 No 3 (2023): July
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeemi.v5i3.307

Abstract

Identifying synergistic drug combinations in cancer treatment is challenging due to the complex molecular circuitry of cancer and the exponentially increasing number of drugs. Therefore, computational approaches for predicting drug synergy are crucial in guiding experimental efforts toward finding rational combination therapies. This research selects the molecular features of cancer cells with a diffusion network-based approach. Additionally, a model is developed using non-linear regression algorithms, namely Random Forest, Extremely Randomized Tree, and XGBoost, to predict the synergy score of drug combinations against the selected cancer cell features. The data used are drug combination screening data and cancer cell molecules provided by AstraZeneca-Sanger DREAM Challenge. The feature selection results demonstrate the relevance of cancer cell molecular features selected by the diffusion network. The prediction results indicate that the Random Forest algorithm shows a good correlation value of 0.570 in the model with a small dataset. In contrast, for the model with an instance or row size larger than the number of features or columns, the XGBoost algorithm achieves a good correlation value of 0.932. INDEX TERMS cancer, drug combination, drug synergy, network diffusion kernel, non-linear regression.
Modeling Human Mobility by Train on the Spread of COVID-19 in East Java Province Using Distance-Decay PageRank Algorithm Rizha Al-Fajri; Medria Kusuma Dewi Hardhienata; Yeni Herdiyeni
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.v9i4.27285

Abstract

Since early 2020, the world has been dealing with the COVID-19 outbreak. A person who has been infected with COVID-19 has the potential to transmit the virus to others. This study aims to model human mobility by train using the spatial network in East Java Province. This research examines the relationship between human mobility by train and the spread of COVID-19 in East Java Province. The spatial network is formed based on train stations and train trips, and the model was created using the Distance-decay PageRank algorithm. This research has modeled human mobility using the train in East Java Province. The result shows that human mobility by train is highly correlated with the spread of COVID-19 in East Java Province, with a correlation coefficient of 0.7 (r = 0.7).Since early 2020, the world has been dealing with the COVID-19 outbreak. A person who has been infected with COVID-19 has the potential to transmit the virus to others. This study aims to model human mobility by train using the spatial network in East Java Province. This research examines the relationship between human mobility by train and the spread of COVID-19 in East Java Province. The spatial network is formed based on train stations and train trips, and the model was created using the Distance-decay PageRank algorithm. This research has modeled human mobility using the train in East Java Province. The result shows that human mobility by train is highly correlated with the spread of COVID-19 in East Java Province, with a correlation coefficient of 0.7 ( = 0.7).
Perbandingan Algoritma Klasifikasi untuk Mendeteksi Kebutuhan Nitrogen Tanaman Padi Berdasarkan Data Citra Multi-spectral Drone Kahfi Gunardi; Karlisa Priandana; Medria Kusuma Dewi Hardhienata; Wulandari; Mohamad Solahudin
Jurnal Ilmu Komputer dan Agri-Informatika Vol. 10 No. 2 (2023)
Publisher : Departemen Ilmu Komputer, Institut Pertanian Bogor

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/jika.10.2.238-249

Abstract

Optimalisasi penggunaan pupuk Nitrogen (N) sangat penting untuk meningkatkan produktivitas tanaman padi. Untuk mengetahui jumlah pupuk yang diperlukan oleh tanaman padi, petani umumnya menggunakan Bagan Warna Daun (BWD) dengan cara mencocokkan warna daun padi dengan warna pada BWD secara manual. Namun, hal ini sangat memakan waktu. Salah satu strategi untuk meningkatkan efisiensi penentuan kebutuhan pupuk N adalah dengan menggunakan Multi-spectral Drone. Drone digunakan untuk mengambil citra multispectral, kemudian citra ini digunakan untuk menentukan kebutuhan pupuk N. Penelitian ini membandingkan beberapa algoritma klasifikasi untuk memodelkan kebutuhan pupuk N dari data citra multispectral, dengan menggunakan ground truth dari penskalaan BWD. Algoritma klasifikasi yang dibandingkan yaitu Decision Tree (DT), Artificial Neural Network (ANN), Support Vector Machine (SVM), Random Forest (RF), dan K-Nearest Neighbour (KNN). Kinerja kelima algoritma klasifikasi diukur berdasarkan accuracy, recall, precision dan F1 score. Dalam penelitian ini, ditemukan bahwa model klasifikasi yang memiliki kinerja terbaik adalah algoritma Decision Tree (DT) baik dalam perlakuan tanpa normalisasi dan balancing dan dengan normalisasi dan balancing dengan nilai accuracy, recall, precision, dan­­­ F1-score di atas 90%.
Co-Authors Agus Buono Amin Elhan Auzi Asfarian Husin Alatas Iis Rodiah Imas Sukaesih Sitanggang Julio Adisantoso Kahfi Gunardi Karlisa Priandana Kurnianto, Andi Laksono Trisnantoro Michael Julyus Christopher Manullang Mohamad Solahudin Mushthofa Peter John Morley Purnajaya, Akhmad Rezki Qarry Atul Chairunnisa Rizha Al-Fajri Siti Nur Hasanah Sony Hartono Wijaya Syarifah Aini Wisnu Ananta Kusuma WULANDARI Yeni Herdiyeni