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Huzain
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huzain.azis@umi.ac.id
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INDONESIA
Indonesian Journal of Data and Science
Published by yocto brain
ISSN : -     EISSN : 27159930     DOI : -
Core Subject : Science, Education,
Computer Science & IT Decision Sciences, Operations Research & Management Mathematics
IJODAS provides online media to publish scientific articles from research in the field of Data Science, Data Mining, Data Communication, Data Security and Data Representation
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 135 Documents
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[WITHDRAWN] Automatic Face Mask Detection on Gates to Combat Spread Of Covid_19 Dima Genemo, Musa
Indonesian Journal of Data and Science Vol. 3 No. 3 (2022): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v3i3.51

Abstract

The COVID-19 pandemic has spread across the globe, hitting almost every country. To stop the spread of the COVID-19 pandemic, this article introduces face mask detection on a gate to assure the safety of Instructors and students in both class and public places. This work aims to distinguish between faces with masks and without masks. A deep learning algorithm You Only Look Once (YOLO) V5 is used for face mask detection and classification. This algorithm detects the faces with and without masks using the video frames from the surveillance camera. The model trained on over 800 video frames. The sequence of a video frame for face mask detection is fed to the model for feature acquisition. Then the model classifies the frames as faces with a mask and without a mask. We used loss functions like Generalize Intersection of Union for abjectness and classification accuracy. The datasets used to train the model are divided as 80% and 20% for training and testing, respectively. The model has provided a promising result. The result found shows accuracy and precision of 95% and 96%, respectively. Results show that the model performance is a good classifier. The successful findings indicate the suggested work's soundness.
Analisis Quality of Service Layanan Video Surveillance Area Traffic Control System (ATSC) Pada Jaringan Internet Dinas Perhubungan Kota Kendari Nur bahri, Nur Bahri; Salim, Yulita; Azis, Huzain
Indonesian Journal of Data and Science Vol. 3 No. 3 (2022): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v3i3.52

Abstract

Dinas Perhubungan Kota Kendari menjadi salah satu kota yang telah menerapkan teknologi ATCS. Proses pemantau dilakukan menggunakan CCTV melalui jaringan internet yang dipantau secara real time melalui ruang kontrol Dinas Perhubungan Kota Kendari. Penerapan layanan video surveilance ATCS pada dinas perhubungan kota Kendari masih sering terjadi kendala seperti akses video surveillance yang dilakukan secara real-time mengalami buffering sehingga kualitas video yang ditampilkan tidak optimal. Permasalahan yang terjadi tersebut perlu dilakukan tindak lanjut penanganan dengan melakukan analisa layanan atau yang dikenal dengan Quality of Service. untuk menentukan apakah kualitas jaringan pada Layanan Video surveillance ATCS yang digunakan telah sesuai atau perlu dilakukan peningkatan kualitas sesuai standarisasi Tiphon dengan menggunakan metode Action Research (AR). Hasil penelitian menunjukkan hasil dari penguuran jaringan dinas Perhubungan Kota Kendari mendapatkan nilai QoS “3,55” dengan indeks “memuaskan” dan Pada Provider data (Tri) dengan nilai QoS “3,31” dengan kategori “memuaskan” yang telah di kategorikan pada standarisasi Tiphon.
[WITHDRAWN] Deep Reinforcement Learning for Tehran Stock Trading Yousefi, Neda
Indonesian Journal of Data and Science Vol. 3 No. 3 (2022): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v3i3.53

Abstract

One of the most interesting topics for research and also for making a profit is stock trading. Artificial intelligence has had a great impact on this path. A lot of research has been done to investigate the application of machine learning, and deep learning methods in stock trading. Despite the large amount of research done in the field of prediction and automation trading, stock trading as a deep reinforcement-learning problem remains an open research area. The progress of reinforcement learning as well as the intrinsic properties of reinforcement learning make it a suitable method for market trading in theory. In this paper, single stock trading models are presented based on the fine-tuned state-of-the-art deep reinforcement learning algorithms (Deep Deterministic Policy Gradient (DDPG) and Advantage Actor Critic (A2C)). These algorithms are able to interact with the trading market and capture the financial market dynamics. The proposed models are compared, evaluated, and verified on historical stock trading data. Annualized return and Sharpe ratio have been used to evaluate the performance of proposed models. The results show that the agent designed based on both algorithms is able to make intelligent decisions on historical data. The DDPG strategy performs better than the A2C and achieves better results in terms of convergence, stability, and evaluation criteria.
Analisis Performa Metode Gaussian Naïve Bayes untuk Klasifikasi Citra Tulisan Tangan Karakter Arab Nurul A'ayunnisa; Salim, Yulita; Azis, Huzain
Indonesian Journal of Data and Science Vol. 3 No. 3 (2022): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v3i3.54

Abstract

Berdasarkan penelitian yang dilakukan oleh Herman dkk., peneliti mencoba mengangkat kembali metode yang diterapkan dengan menggunakan dataset yang berbeda dan dengan jumlah yang lebih banyak. Penelitian ini bertujuan untuk menghitung performa metode (akurasi, presisi, recall, dan f-measure) Gaussian Naïve Bayes. Dataset yang digunakan adalah citra tulisan tangan karakter arab. Berdasarkan hasil perhitungan performa menunjukkan tingkat akurasi tertinggi sebesar 12%, presisi 10%, recall 12%, dan f-measure 8%.
Design of a Sales Performance System for SMEs based on Business Intelligence and Data Warehouse Saputra, Dhanar; Subarkah, Pungkas; Afifah, Erika Luthfi; Muflikhatun, Siti; Ramadani, Nevita Cahaya; Utami, Melida Ratna; Aunillah, Puteri Johar
Indonesian Journal of Data and Science Vol. 3 No. 3 (2022): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v3i3.58

Abstract

The influence of information technology today is powerful. It impacts people's lives because technological changes are running so fast and affect the way of thinking and behaving in competition in the business world and organizations. Small and Medium Enterprises (SMEs) must be able to adapt to this technology to maintain their business. It means that digitizing SMEs means integrating technology into all business activities. In this study, Toko Cerme is the object of research. The Toko Cerme is a SMEs in the form of a minimarket located in Central Java, Indonesia. The Toko Cerme takes advantage of technology to help run business processes so that they can be managed optimally. In running its business, The Toko Cerme is currently using an information system to input product data and transaction activities. The purpose of this research is to propose a Design of a Sales Performance System based on Business Intelligence and a Data Warehouse to support business processes at the Toko Cerme so that it can efficiently process data and information in the future. From the research that the authors conducted, it can be concluded that the results of this study are the creation of a data warehouse and business intelligence design using the nine steps Kimbal method. At the same time, Pentaho Data Integration (PDI) is a tool. The design is used as a reference in producing information relating to sales transactions.
Detecting Harmful Activity in Hajj Plagiarism Using Deep Learning Musa
Indonesian Journal of Data and Science Vol. 4 No. 1 (2023): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v4i1.59

Abstract

CCTV surveillance is the most extensively used intelligent latest innovation. The use of surveillance cameras has risen dramatically because of the convenience of monitoring from anywhere and the reduction of crime rates in public areas. In this paper, we introduce the idea of bad vibe activity detection from live videos to enhance the security and safety of pilgrims. The proposed bad vibes activity recognition model is intended to be addressed in the most efficient manner possible using cutting-edge technologies such as TensorFlow and Keras. TensorFlow was chosen because the project could be deployed to a mobile environment in the future with the possibility of extension of other areas such as airport security, bus stain, and public areas that may deserve special attention for security checks. We choose MediaPipe Holistic for employee bad vibe recognition in the model.
Diagnosis of Hepatitis Using Supervised Learning Algorithm Musa
Indonesian Journal of Data and Science Vol. 4 No. 1 (2023): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v4i1.60

Abstract

Hepatitis is the most serious disease in developing countries. Therefore, early diagnosis is very important to obstacle the effect that can happen as a consequence of this disease. In this case, deep learning can solve the issue at an early stage. An innovative deep learning-based technique to identify hepatitis is presented in this study. In this study 45 layers, convolutional neural network (CNN) architecture connected with three fully connected layers is used in the proposed architecture. The two classes of collected hepatitis datasets are then used to train the suggested CNN model. The model achieved 0.934 classification accuracy. The proposed model was compared to the state of the art at the time. The outcome presented implies that the model's performance is remarkable.
Prediksi Potensi Donatur Menggunakan Model Logistic Regression jabir, sitti rahmah; Azis, Huzain; Widyawati, Dewi; Tenripada, Andi Ulfa
Indonesian Journal of Data and Science Vol. 4 No. 1 (2023): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v4i1.64

Abstract

GRDS menghadapi kelangkaan dana, ketika diperlukan untuk merawat para korban Gaja. Gaja adalah topan bernama kelima dari musim siklon Samudra Hindia Utara 2018 yang mempengaruhi sebagian besar tempat di Tamil Nadu, India selama bulan November 2018. Tujuan dari penelitian ini adalah untuk menggunakan riwayat donasi untuk menganalisis apakah donator akan menyumbang atau tidak menggunakan regresi logistik. Data Tamil Nadu diberikan untuk menerapkan model yang dibangun untuk memprediksi donator yang paling mungkin menjadi korban topan Gaja. Pada tahap pengumpulkan data seringkali terjadi hambatan, salah satu hambatannya yaitu fenomena missing data atau data hilang. Akibat dari adanya missing data adalah pendugaan parameter menjadi tidak efisien. Ukuran data yang berkurang dapat mengakibatkan kesulitan dalam menganalisis, sehingga hasil yang didapatkan menjadi tidak valid dan tujuan dari penelitian tidak tercapai. Data yang hilang akan diisi menggunakan metode single imputation. Data yang telah diimputasi menggunakan beberapa metode akan membantu dalam melakukan prediksi. Dimana algoritma yang digunakan untuk melakukan prediksi ialah logistic regression. Beberapa data dihilangkan setelah melihat multikolinearitas. Dalam tahap pemodelan, data dibagi menjadi 2 yaitu 70% untuk data pelatihan dan 30% untuk data tes. Dimana hasil perhitungan akurasi dari model ialah 0,6129 yang menunjukkan bahwa model tidak melakukan prediksi dengan baik menggunakan metode tersebut.
Analisis Sentimen Pandangan Public Profesi PNS (Pegawai Negeri Sipil) dari Twiter menerapkan indonesian Roberta Base Sentiment Classifier Jaya, Alwi
Indonesian Journal of Data and Science Vol. 4 No. 1 (2023): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v4i1.66

Abstract

Penelitian ini dilakukan untuk mendapatkan opini masyarakat terkait profesi Pegawai Negeri Sipil (PNS) yang beredar di media sosial twitter dengan menggunakan metode algoritma Bidirectional Encoder Representations from Transformers (BERT) yang di mana dalam Bahasa Indonesia berarti representasi dua arah encoder. BERT berguna untuk mengolah representasi dua arah yang berada dalam teks tanpa nama dengan menggabungkan sisi kanan dan kiri pada sebuah konteks dalam segala bagian.yang di gunakan untuk menentukan positif, negatif atau neutralnya pandangan masyarakat terkait profesi PNS yang beredar di media social twitter dengan meharapkan Hasil beruapa pandangan masyarakat terhadap profesi PNS dengan mengumpulkan 394 tweet yang diambil dari media sosial twitter kita bisa mendapatkan hasil kesimpulan berupa berapa persen orang yang memandang positif profesi PNS dan berapa persen orang yang memandang negatif profesi PNS.
Analisis performa metode Naïve Bayesh Classifier pada Electronic Nose dalam identifikasi formalin pada tahu Admojo, Fadhila Tangguh; Jabir, Siti Rahma
Indonesian Journal of Data and Science Vol. 4 No. 1 (2023): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v4i1.67

Abstract

Penelitian ini bertujuan untuk menganalisis performa metode Naive Bayes Classifier (NBC) dalam identifikasi formalin pada tahu menggunakan Electronic Nose. Hasil dari penelitian ini menunjukkan bahwa performa NBC cukup moderat, dengan nilai akurasi sekitar 0,59 hingga 0,60, presisi sekitar 0,67 hingga 0,68, recall sekitar 0,59 hingga 0,60, dan F1-score sekitar 0,55. Ini menunjukkan bahwa model mampu mengklasifikasikan beberapa titik data dengan benar, tetapi tidak semua. Walaupun demikian masih ada ruang untuk perbaikan dan perlu dipertimbangkan untuk mencoba metode lain untuk meningkatkan hasil identifikasi formalin pada tahu. Hasil ini menunjukkan bahwa metode Naive Bayes Classifier pada Electronic Nose masih belum dapat memberikan hasil yang optimal dalam identifikasi formalin pada tahu, dan hasil yang diperoleh masih tidak lebih baik dari penelitian sebelumnya

Page 5 of 14 | Total Record : 135

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