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All Journal Dinamik Semantik Techno.Com: Jurnal Teknologi Informasi Telematika : Jurnal Informatika dan Teknologi Informasi Jurnas Nasional Teknologi dan Sistem Informasi JAIS (Journal of Applied Intelligent System) Proceeding SENDI_U Journal of Information System Sistemasi: Jurnal Sistem Informasi Journal of Applied Geospatial Information Sinkron : Jurnal dan Penelitian Teknik Informatika JURNAL MEDIA INFORMATIKA BUDIDARMA Jurnal Eksplora Informatika JOURNAL OF APPLIED INFORMATICS AND COMPUTING Techne : Jurnal Ilmiah Elektroteknika Jurnal Sistem Cerdas EDUMATIC: Jurnal Pendidikan Informatika Building of Informatics, Technology and Science Abdimasku : Jurnal Pengabdian Masyarakat Jurnal Teknik Informatika (JUTIF) Journal of Applied Computer Science and Technology (JACOST) Advance Sustainable Science, Engineering and Technology (ASSET) Journal La Multiapp Community: Jurnal Pengabdian Pada Masyarakat
Ardytha Luthfiarta
Teknik Informatika S1, Fakultas Ilmu Komputer, Universitas Dian Nuswantoro
Aerospace Engineering Automotive Engineering Chemical Engineering, Chemistry & Bioengineering 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 Materials Science & Nanotechnology Physics Social Sciences Other

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Journal : JURNAL MEDIA INFORMATIKA BUDIDARMA

 1 
Improving Infant Cry Recognition with CNNs and Imbalance Mitigation Indrawan, Michael; Luthfiarta, Ardytha; Fahreza, Muhammad Daffa Al; Rafid, Muhammad
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 2 (2024): April 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i2.7370

Abstract

The classification of baby cries using machine learning is essential for developing automated systems that can assist caregivers in identifying and responding to the needs of infants promptly and accurately. This study aims to improve upon previous research relating to the Cry Baby Dataset, which has highly imbalanced data. We combine oversampling and undersampling techniques using SMOTE and ENN, along with data augmentation through pitch shifting and noise addition to address the data imbalance issue. The processed data was then modeled using Convolutional Neural Networks (CNN). The study yielded an overall accuracy of 88%, with balanced accuracy observed across all classes, effectively mitigating data imbalance. This represents a notable advancement compared to previous research, which often encountered challenges with unbalanced accuracies across classes. The classes identified include recordings of baby cries attributed to belly pain caused by colic, recordings related to burping, recordings associated with discomfort or other symptoms, recordings of hungry baby cries, and recordings indicating fatigue or the need for sleep. This shows a significant improvement from previous studies, which had very unbalanced accuracy for each class.
Optimizing Sentiment Analysis of Working Hours Impact on Generation Z’s Mental Health Using Backpropagation Farsya, Nabila Zibriza; Luthfiarta, Ardytha; Maharani, Zahra Nabila; Ganiswari, Syuhra Putri
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 3 (2024): Juli 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i3.7827

Abstract

The topic of working hours' impact, Generation Z, and mental health are discussions that are often found on social media such as X (used to be Twitter). The sentiment analysis addressing these topics is needed to find out people’s opinions regarding these topics. It could also be helpful as a consideration for the decision-making process for related topics research. Therefore, this research aims to improve the accuracy of the model generated by the previous sentiment analysis research regarding the working hours’ impact on Gen Z’s mental health. The contribution of this research is by building a robust Backpropagation Neural Network model and utilizing SMOTETomek to achieve higher accuracy. This research compared two oversampling techniques for data balancing: SMOTE and SMOTETomek. The result shows that this research has successfully outperformed the baseline research with the best accuracy of 91% using SVM by generating the best accuracy of 93.01% with SMOTETomek. For comparison, SMOTETomek has outperformed SMOTE by generating the best accuracy of 93.01%, while the best accuracy generated with SMOTE is 92.26%. It indicates that in the case of Indonesian text sentiment analysis of this research, SMOTETomek has a better effect compared to SMOTE.
Komparasi Teknik Feature Selection Dalam Klasifikasi Serangan IoT Menggunakan Algoritma Decision Tree Setiawan, Dicky; Nugraha, Adhitya; Luthfiarta, Ardytha
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 1 (2024): Januari 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i1.6987

Abstract

Presence of Internet of Things (IoT) has revolutionized how we interact with the world on our daily life by enabling various devices to connect the internet and transmit data. However, the increasingly widespread use of IoT technology also brings serious threats to cyber security and increases the number of IoT attacks. The need for robust classification models is becoming increasingly clear to anticipate these problems. This research focuses on developing an IoT attack classification model by comparing feature selection techniques that utilize data from the CIC IoT Dataset 2023. This research faces challenges such as data imbalance and the complexity of handling various features. To overcome these challenges, this research uses random undersampling techniques to balance the data and utilizes various feature selection methods, including filter based, wrapper based, and embedded based. Apart from that, this research also tries to use a decision tree algorithm. The findings reveal that the application of wrapper based techniques as feature selection together with a decision tree algorithm produces the highest accuracy of 87.32% in classifying IoT attack types. This emphasizes that the use of techniques and algorithms that are still rarely used can provide fairly good accuracy results.
Optimizing Sentiment Analysis of Working Hours Impact on Generation Z’s Mental Health Using Backpropagation Farsya, Nabila Zibriza; Luthfiarta, Ardytha; Maharani, Zahra Nabila; Ganiswari, Syuhra Putri
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 3 (2024): Juli 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i3.7827

Abstract

The topic of working hours' impact, Generation Z, and mental health are discussions that are often found on social media such as X (used to be Twitter). The sentiment analysis addressing these topics is needed to find out people’s opinions regarding these topics. It could also be helpful as a consideration for the decision-making process for related topics research. Therefore, this research aims to improve the accuracy of the model generated by the previous sentiment analysis research regarding the working hours’ impact on Gen Z’s mental health. The contribution of this research is by building a robust Backpropagation Neural Network model and utilizing SMOTETomek to achieve higher accuracy. This research compared two oversampling techniques for data balancing: SMOTE and SMOTETomek. The result shows that this research has successfully outperformed the baseline research with the best accuracy of 91% using SVM by generating the best accuracy of 93.01% with SMOTETomek. For comparison, SMOTETomek has outperformed SMOTE by generating the best accuracy of 93.01%, while the best accuracy generated with SMOTE is 92.26%. It indicates that in the case of Indonesian text sentiment analysis of this research, SMOTETomek has a better effect compared to SMOTE.
Co-Authors ., Junta Zeniarza ., Junta Zeniarza Abu Salam Abu Salam Adhitya Nugraha Adhitya Nugraha Adhitya Nugraha Affandy Affandy Al Fahreza, Muhammad Daffa Althoff, Mohammad Noval Aris Febriyanto Aryanti, Firda Ayu Dwi Astuti, Yani Parti Bagus Dwi Satya, Mohammad Wahyu Basiron, Halizah Cahya, Leno Dwi Catur Supriyanto Catur Supriyanto Defri Kurniawan Dhita Aulia Octaviani Dzaki, Azmi Abiyyu Dzaki, Muhammad Hafizh Edi Faisal Edi Sugiarto Egia Rosi Subhiyakto, Egia Rosi Erwin Yudi Hidayat Fahreza, Muhammad Daffa Al Fahrezi, Sahrul Fahrezi Fahrezi, Sahrul Yudha Fahri Firdausillah Fairuz Dyah Esabella Farandi, Muhammad Naufal Erza Farsya, Nabila Zibriza Fauzyah, Zahrah Asri Nur Firmansyah, Gustian Angga Ganiswari, Syuhra Putri Hafiizhudin, Lutfi Azis Haresta, Alif Agsakli Harun Al Azies Hasan Shobri Heru Lestiawan Huda, Alam Muhammad Ika Novita Dewi Imam Muttaqin, Almas Najiib Indrawan, Michael Irham Ferdiansyah Katili Ivan Zuhdiansyah Julius Immanuel Theo Krisna Junta Zeniarja Krisna, Julius Immanuel Theo L. Budi Handoko Leno Dwi Cahya Maharani, Zahra Nabila Mahardika, Pramesthi Qisthia Hanum Md. Mahadi Hasan, Md. Mahadi Michael Indrawan Muhammad Daffa Al Fahreza Muhammad Jamhari Muhammad Naufal, Muhammad Muhammad Rafid Mulyana, Yudha Mumtaz, Najma Amira Muttaqin, Almas Najiib Imam Nauval Dwi Primadya Nisa, Laila Rahmatin Novandian, Yohanes Deny Octaviani, Dhita Aulia Primadya, Nauval Dwi Putra, Permana Langgeng Wicaksono Ellwid Putri, Ni Kadek Devi Adnyaswari Rafid, Muhammad Ramadhan Rakhmat Sani Rismiyati Rismiyati Riyanto, Azizu Ahmad Rozaki Sahrul Yudha Fahrezi Salsabila, Pramesya Mutia Satya, Mohammad Wahyu Bagus Dwi Setiawan, Dicky setiawan, nabila putri Soeroso, Dennis Adiwinata Irwan Sri Winarno Sri Winarno Suprayogi Suprayogi Suryaningtyas Rahayu Syarifah, Ulima Muna Utomo, Danang Wahyu Wibowo Wicaksono Wibowo Wicaksono Wulandari, Kang Andini Wulandari, Kang, Andini Zarifa, Yasmine Zuhdiansyah, Ivan