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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) JOIN (Jurnal Online Informatika) MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Jurnal Restikom : Riset Teknik Informatika dan Komputer Jurnal Pengabdian Kepada Masyarakat Abdi Nusa Jurnal Pengabdian Kepada Masyarakat Abdi Putra Fidelity : Jurnal Teknik Elektro International Journal Engineering and Applied Technology (IJEAT)
Jelita Asian
Universitas Nusa Putra
Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering Mechanical Engineering Public Health Other

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Comparison of feature extraction and auto-preprocessing for chili pepper (Capsicum Frutescens) quality classification using machine learning Asian, Jelita; Arianti, Nunik Destria; Ariefin, Ariefin; Muslih, Muhamad
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 1: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i1.pp319-328

Abstract

The low-cost camera for machine vision, such as a webcam, still has a problem with resolution noise. Therefore, it is important to learn strategies to reduce noise from low-cost camera images so that they can be widely used for grading machines in the future. This paper aims to compare three feature extraction methods with auto-preprocessing to classify chili pepper (Capsicum Frutescens) quality using a machine learning algorithm. Three extraction methods were used, including the color feature, oriented FAST and rotated BRIEF (ORB), and the combination color feature and ORB. A total of 525 image data for quality chili pepper were collected using the webcam. The auto-preprocessing strategy to classify chili peppers can improve the performance of machine-learning algorithms for all data generated by the feature extractor. The performance of the chili paper quality classification model with auto-preprocessing of the variable color feature can improve the performance of machine learning algorithms by up to 64.21%. The performance improvement of the classification model using the ORB feature variable and the auto-preprocessing of up to 4.41%. The performance improvement of the classification model using machine learning algorithms is 11.27% when using the combination color feature and ORB feature and auto-preprocessing.
Data Exfiltration Anomaly Detection on Enterprise Networks using Deep Packet Inspection Asian, Jelita; Erlangga, Dimas; Ayu, Media
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol 22 No 3 (2023)
Publisher : LPPM Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v22i3.3089

Abstract

Advanced persistent threats (APT) are threat actors with the advanced Technique, Tactic and Procedure (TTP) to gain covert control of the computer network for a long period of time. These threat actors are the highest cyber attack risk factor for enterprise companies and governments. A successful attack by the APT threat Actors has the capabilities to do physical damage. APT groups are typically state-sponsored and are considered the most effective and skilled cyber attackers. The final goal for the APT Attack is to exfiltrate victims data or sabotage system. This aim of this research is to exercise multiple Machine Learning Approach such as k-Nearest Neighbors and H20 Deep Learning Model and also employ Deep Packet Inspection on enterprise network traffic dataset in order to identify suitable approaches to detect data exfiltration by APT threat Actors. This study shows that combining machine learning techniques with Deep Packet Inspection significantly improves the detection of data exfiltration attempts by Advanced Persistent Threat (APT) actors. The findings suggest that this approach can enhance anomaly detection systems, bolstering the cybersecurity defenses of enterprises. Consequently, the research implications could lead to developing more robust strategies against sophisticated and covert cyber threats posed by APTs.
Analisis Pembuatan Gula Semut Aren UMKM Desa Cikahuripan Kecamatan Cisolok Kabupaten Sukabumi Sholahudin; Pipin Adelina; Pebi Andriansyah; Siti Syarah Sarmila; Edwinanto; Jelita Asian; Dini Oktarina DH
Jurnal Pengabdian Kepada Masyarakat Abdi Putra Vol 4 No 1 (2024): Januari 2024
Publisher : Universitas Nusa Putra & Persatuan Insinyur Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52005/abdiputra.v4i1.179

Abstract

Gula aren adalah salah satu komoditi unggulan di Kecamatan Cisolok. Hal ini didukung dengan lahan aren milik perkebunan rakyat di kecamatan ini adalah terluas kedua di Kabupaten Sukabumi. Oleh karena banyaknya produsen aren di daerah ini, peluang strategi usaha gula aren menjadi besar. UMKM gula semut di Desa Cikahuripan menjadi salah satu contoh pendobrak usaha gula aren dengan bentuk yang lebih modern. Sayangnya, unit usaha semacam ini ialah satu-satunya di desa ini. Dengan demikian, peran mahasiswa dalam menggalangkan eksplorasi dan sosialisasi terkait potensi daerah sangat diperlukan. Penulis dan tim telah melaksanakan pengabdian di desa ini dan berkesempatan untuk melakukan observasi pada UMKM tersebut. Dengan adanya kegiatan tersebut, telah diperoleh hasil analisis proses pembuatan gula semut aren. Harapannya selain melalui media sosial, karya tulis ini juga turut meramaikan masyarakat akan besarnya potensi gula semut aren.
Teknik Data Mining untuk Prediksi Kanker Payudara yang Efisien Asian, Jelita; Solikin, Irfan
Fidelity : Jurnal Teknik Elektro Vol 3 No 3 (2021): Edisi September 2021
Publisher : Universitas Nusa Putra

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52005/fidelity.v3i3.100

Abstract

Salah satu keganasan paling umum pada wanita, kanker payudara, juga merupakan salah satu penyebab utama kematian. Menurut Organisasi Kesehatan Dunia, kanker payudara sekarang menjadi keganasan paling umum di antara wanita di seluruh dunia. Untuk menyelamatkan nyawa, identifikasi dini kanker payudara sangat penting. Akurasi klasifikasi dari database Wisconsin Breast Cancer (WBC) digunakan untuk membandingkan berbagai pengklasifikasi Data Mining dalam penelitian ini. Bertujuan untuk akurasi prediksi yang tinggi, pekerjaan ini bermaksud untuk mengembangkan model klasifikasi akurat untuk prediksi kanker payudara yang sepenuhnya menggunakan informasi berharga yang ditemukan dalam data klinis. Berdasarkan data WBC, kami telah menjalankan uji coba. Ini dibagi menjadi dua set: set latihan 499 pasien dan set tes dunia nyata 200. Menggunakan perangkat lunak Weka, eksperimen ini menganalisis enam strategi kategorisasi dan menemukan bahwa Support Vector Machine (SVM) lebih akurat dalam memprediksi masa depan daripada teknik lain yang diuji. Keakuratan beberapa teknologi deteksi kanker payudara sedang diselidiki dan dibandingkan. SVM lebih cocok untuk menangani kesulitan klasifikasi seperti prediksi kanker payudara. Jadi kami menyarankan untuk menerapkan temuan ini pada masalah klasifikasi lainnya juga
A Bibliometric Exploration of Artificial Intelligence and Cybersecurity Research: Trends, Collaborations, and Impact (2013-2023) Asian, Jelita
Fidelity : Jurnal Teknik Elektro Vol 6 No 2 (2024): Edition for May 2024
Publisher : Universitas Nusa Putra

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52005/fidelity.v6i2.225

Abstract

This study delves into the burgeoning field of AI and cybersecurity research, utilizing bibliometric analysis to uncover key trends, collaborations, and influential contributions from 2013 to 2023. By employing advanced techniques such as citation analysis, co-authorship networks, and keyword co-occurrence, we identify pivotal publications, leading research institutions, and emerging areas of focus. The analysis provides a comprehensive overview of the intellectual landscape, highlighting the evolution of research themes, the geographic distribution of research activity, and the extent of international collaborations. The findings offer valuable insights for researchers, policymakers, and funding agencies seeking to understand the current state and future trajectory of AI and cybersecurity research.
Data Exfiltration Anomaly Detection on Enterprise Networks using Deep Packet Inspection Jelita Asian; Dimas Erlangga; Media Ayu
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 22 No. 3 (2023)
Publisher : LPPM Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v22i3.3089

Abstract

Advanced persistent threats (APT) are threat actors with the advanced Technique, Tactic and Procedure (TTP) to gain covert control of the computer network for a long period of time. These threat actors are the highest cyber attack risk factor for enterprise companies and governments. A successful attack by the APT threat Actors has the capabilities to do physical damage. APT groups are typically state-sponsored and are considered the most effective and skilled cyber attackers. The final goal for the APT Attack is to exfiltrate victims data or sabotage system. This aim of this research is to exercise multiple Machine Learning Approach such as k-Nearest Neighbors and H20 Deep Learning Model and also employ Deep Packet Inspection on enterprise network traffic dataset in order to identify suitable approaches to detect data exfiltration by APT threat Actors. This study shows that combining machine learning techniques with Deep Packet Inspection significantly improves the detection of data exfiltration attempts by Advanced Persistent Threat (APT) actors. The findings suggest that this approach can enhance anomaly detection systems, bolstering the cybersecurity defenses of enterprises. Consequently, the research implications could lead to developing more robust strategies against sophisticated and covert cyber threats posed by APTs.
Data Exfiltration Anomaly Detection on Enterprise Networks using Deep Packet Inspection Asian, Jelita; Erlangga, Dimas; Ayu, Media
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 22 No. 3 (2023)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v22i3.3089

Abstract

Advanced persistent threats (APT) are threat actors with the advanced Technique, Tactic and Procedure (TTP) to gain covert control of the computer network for a long period of time. These threat actors are the highest cyber attack risk factor for enterprise companies and governments. A successful attack by the APT threat Actors has the capabilities to do physical damage. APT groups are typically state-sponsored and are considered the most effective and skilled cyber attackers. The final goal for the APT Attack is to exfiltrate victims data or sabotage system. This aim of this research is to exercise multiple Machine Learning Approach such as k-Nearest Neighbors and H20 Deep Learning Model and also employ Deep Packet Inspection on enterprise network traffic dataset in order to identify suitable approaches to detect data exfiltration by APT threat Actors. This study shows that combining machine learning techniques with Deep Packet Inspection significantly improves the detection of data exfiltration attempts by Advanced Persistent Threat (APT) actors. The findings suggest that this approach can enhance anomaly detection systems, bolstering the cybersecurity defenses of enterprises. Consequently, the research implications could lead to developing more robust strategies against sophisticated and covert cyber threats posed by APTs.
Sentiment Analysis for the Brazilian Anesthesiologist Using Multi-Layer Perceptron Classifier and Random Forest Methods Asian, Jelita; Dholah Rosita, Moneyta; Mantoro, Teddy
JOIN (Jurnal Online Informatika) Vol 7 No 1 (2022)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v7i1.900

Abstract

Sexual harassment is defined as giving sexual attention both verbally, either in speech or writing, and physically to victims who are predominantly women, On July 13, 2022, there was a tweet featuring a video of sexual harassment that made it trend in various countries. The video irritated Twitter users and made various comments resulting in various sentiments that can be analyzed using sentiment analysis. The purpose of this study is to see what the public thinks about the sexual harassment case of Brazilian anesthesiologist. Besides the sentiment analysis, another aim of this study is to see how objective are those sentiments based on their polarity. This study uses a comparison of two methods in sentiment analysis, namely Multi-Layer Perceptron Classifier and Random Forest, and labeling automatically using TextBlob.  This results in 94.44% accuracy, 94.44% precision, 92% recall and 93% f1_score. For MLP Classifier and accuracy 96.42%, precision 94.44%, recall 96.66% and f1_score 95.56% for Random Forest. Sentiment polarity score from the TextBlob is -0.5 and subjectivity is 0.4 which indicates that most statements are negative and subjective score is 0.4, which means those sentiments are subjective in nature.
Co-Authors Aan Setiawati adang badru jaman,anggun fergina, adang badru jaman,anggun fergina Alex Alun Sujjada Alvira Fauziah Rahmah Alyanissa Putri Iskandar Ammar Ammar Ariefin, Ariefin Arif Rakhman Suharso Ayu, Media Dea Kris Sintiana Putri Debby Rahmawati Dede Wahidin Deshinta Arovva Dewi deshinta.arrovadewi Deudeu Sri Rahayu Dholah Rosita, Moneyta Dimas Erlangga Dini Oktarina DH Dini Oktarina Dwi Handayani Edwinanto Ekky Noor Mahmudi Eneng Siti Nasiroh Erlangga, Dimas Esa Nurfaizah Fadillah Alviqih Feri Fahmi Al Gadri Gina Syabani Yuda Gisni Ariyanti Gustian, Dudih Ibnu fathurrohman Fathurrohman Ihsan Nurhuda Indri Febriani M Dezan Sya’ban S Mayang Gunawan Media Ayu Mega Putri Utami Moneyta Dholah Rosita Muhammad Ikrar Dharda Muharrik Mupaat Muslih, Muhamad Novohatko Elena Nivolaevna Nunik Destria Arianti Pebi Andriansyah Pipin Adelina Raziya Mutasim Rico Sihotang Rizal Fadli Sagita Widya Selsa Khairunisa Fadiya haya Sholahudin Siti Syarah Sarmila Solikin, Irfan Somantri Somantri Taufik Rido Teddy Mantoro Tubagus Dzikril Umar Aditiawarman Usnul Latipah Vemi Januarita Putri Yudi Nata Zulia nur permatasari