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All Journal Jurnal Simetris Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) OPSI Scientific Journal of Informatics Seminar Nasional Informatika (SEMNASIF) Annual Research Seminar InfoTekJar : Jurnal Nasional Informatika dan Teknologi Jaringan Syntax Literate: Jurnal Ilmiah Indonesia Jurnal Ilmiah Matrik Prosiding Seminar Nasional Sains dan Teknologi Terapan Jurnal Manajemen Informatika dan Sistem Informasi Journal of Information Systems and Informatics Teknomatika (Jurnal Teknologi dan Informatika) Journal of Information Technology Ampera Journal of Computer and Information Systems Ampera Journal of Software Engineering Ampera Jurnal Pengembangan Sistem Informasi dan Informatika Jurnal Ilmiah Sistem Informasi dan Teknik Informatika (JISTI) Jurnal Bina Komputer Jurnal Maklumatika Jurnal Informatika: Jurnal Pengembangan IT E-Amal: Jurnal Pengabdian Kepada Masyarakat Indonesia Bergerak: Jurnal Hasil Kegiatan Pengabdian Masyarakat
Ari Muzakir
Universitas Bina Darma
Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Public Health Social Sciences Other

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Journal : Scientific Journal of Informatics

 1 
Model Data Mining sebagai Prediksi Penyakit Hipertensi Kehamilan dengan Teknik Decision Tree Muzakir, Ari; Wulandari, Rika Anisa
Scientific Journal of Informatics Vol 3, No 1 (2016): May 2016
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v3i1.4610

Abstract

Prevalensi hipertensi pada wanita hamil terjadi sebanyak 1.062 kasus (12,7%). Dari 1062 kasus ibu hamil dengan hipertensi, ditemukan 125 kasus (11,8%) yang telah didiagnosis dengan hipertensi oleh tenaga kesehatan. RSIA YK Madira Palembang sebagai pusat kesehatan harus mengembangkan metode yang dapat memprediksi risiko tinggi ibu hamil dengan hipertensi dari data hasil pemeriksaan kehamilan. Dengan memanfaatkan sumber data yang terdiri dari data perawatan antenatal, diterapkan teknik data mining dengan algoritma decision tree C4.5, berdasarkan Knowledge Discovery in Database (KDD). Sehingga akan ditemukan pengetahuan, informasi, dan pola tersembunyi dari data pelayanan antenatal, yang merupakan prediksi hipertensi pada kehamilan. Metode yang digunakan yaitu Algoritma C4.5. Setelah mendapatkan decision tree dan rules yang dapat memprediksi penyakit hipertensi dalam kehamilan, dilakukan evaluasi dengan supplied test set menggunakan WEKA dihasilkan kesalahan (error) 7.3427% dan tingkat akurasi 92.6573%. Data training yang berjumlah 286 instances, hal ini menunjukkan bahwa terdapat 265 instances yang akurat dan 21 instances yang error atau prediksinya salah. 
Watermarking Techniques Using Least Significant Bit Algorithm for Digital Image Security Standard Solution- Based Android Muzakir, Ari; Habibi, Mailan
Scientific Journal of Informatics Vol 4, No 1 (2017): May 2017
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v4i1.7290

Abstract

Ease of deployment of digital image through the internet has positive and negative sides, especially for owners of the original digital image. The positive side of the ease of rapid deployment is the owner of that image deploys digital image files to various sites in the world address. While the downside is that if there is no copyright that serves as protector of the image it will be very easily recognized ownership by other parties. Watermarking is one solution to protect the copyright and know the results of the digital image. With Digital Image Watermarking, copyright resulting digital image will be protected through the insertion of additional information such as owner information and the authenticity of the digital image. The least significant bit (LSB) is one of the algorithm is simple and easy to understand. The results of the simulations carried out using android smartphone shows that the LSB watermarking technique is not able to be seen by naked human eye, meaning there is no significant difference in the image of the original files with images that have been inserted watermarking. The resulting image has dimensions of 640x480 with a bit depth of 32 bits. In addition, to determine the function of the ability of the device (smartphone) in processing the image using this application used black box testing. 
Model Data Mining sebagai Prediksi Penyakit Hipertensi Kehamilan dengan Teknik Decision Tree Muzakir, Ari; Wulandari, Rika Anisa
Scientific Journal of Informatics Vol 3, No 1 (2016): May 2016
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v3i1.4610

Abstract

Prevalensi hipertensi pada wanita hamil terjadi sebanyak 1.062 kasus (12,7%). Dari 1062 kasus ibu hamil dengan hipertensi, ditemukan 125 kasus (11,8%) yang telah didiagnosis dengan hipertensi oleh tenaga kesehatan. RSIA YK Madira Palembang sebagai pusat kesehatan harus mengembangkan metode yang dapat memprediksi risiko tinggi ibu hamil dengan hipertensi dari data hasil pemeriksaan kehamilan. Dengan memanfaatkan sumber data yang terdiri dari data perawatan antenatal, diterapkan teknik data mining dengan algoritma decision tree C4.5, berdasarkan Knowledge Discovery in Database (KDD). Sehingga akan ditemukan pengetahuan, informasi, dan pola tersembunyi dari data pelayanan antenatal, yang merupakan prediksi hipertensi pada kehamilan. Metode yang digunakan yaitu Algoritma C4.5. Setelah mendapatkan decision tree dan rules yang dapat memprediksi penyakit hipertensi dalam kehamilan, dilakukan evaluasi dengan supplied test set menggunakan WEKA dihasilkan kesalahan (error) 7.3427% dan tingkat akurasi 92.6573%. Data training yang berjumlah 286 instances, hal ini menunjukkan bahwa terdapat 265 instances yang akurat dan 21 instances yang error atau prediksinya salah.
Push-Up Detector Applications Using Quality Function Development and Anthropometry for Movement Error Detection Muzakir, Ari; Kusmindari, Christofora Desi
Scientific Journal of Informatics Vol 5, No 2 (2018): November 2018
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v5i2.16332

Abstract

Push-up is the simplest and most widely performed sport. Although simple, it also has a high risk of injury risk if done not in accordance with the rules. Push-up detector is a good push-up motion monitoring solution. In this way, nonstandard movements can be detected and corrected immediately. It has two motion sensors integrated with Arduino-based microcontroller. From this detector tool got the data of push-up result from sensor mounted. Sensor data will be displayed in the application in real-time. Quality function development is used to determine the criteria of the user. The sample data involved 200 participants who followed the testing of this tool and got 90% who can do the push-up correctly. Factors that affect the height, age, and weight. Tests conducted on adolescent boys aged 18-23 years. The results of this study is an application capable of monitoring each push-up movement to position in accordance with the provisions to minimize injuries resulting from movement errors.
WATERMARKING TECHNIQUES USING LEAST SIGNIFICANT BIT ALGORITHM FOR DIGITAL IMAGE SECURITY STANDARD SOLUTION- BASED ANDROID Muzakir, Ari; Habibi, Mailan
Scientific Journal of Informatics Vol 4, No 1 (2017): May 2017
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v4i1.7290

Abstract

Ease of deployment of digital image through the internet has positive and negative sides, especially for owners of the original digital image. The positive side of the ease of rapid deployment is the owner of that image deploys digital image files to various sites in the world address. While the downside is that if there is no copyright that serves as protector of the image it will be very easily recognized ownership by other parties. Watermarking is one solution to protect the copyright and know the results of the digital image. With Digital Image Watermarking, copyright resulting digital image will be protected through the insertion of additional information such as owner information and the authenticity of the digital image. The least significant bit (LSB) is one of the algorithm is simple and easy to understand. The results of the simulations carried out using android smartphone shows that the LSB watermarking technique is not able to be seen by naked human eye, meaning there is no significant difference in the image of the original files with images that have been inserted watermarking. The resulting image has dimensions of 640x480 with a bit depth of 32 bits. In addition, to determine the function of the ability of the device (smartphone) in processing the image using this application used black box testing.
Model for Identification and Prediction of Leaf Patterns: Preliminary Study for Improvement Muzakir, Ari; Ependi, Usman
Scientific Journal of Informatics Vol 8, No 2 (2021): November 2021
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v8i2.30024

Abstract

Purpose: Many studies have conducted studies related to automation for image-based plant species identification recently. Types of plants, in general, can be identified by looking at the shape of the leaves, colors, stems, flowers, and others. Not everyone can immediately recognize the types of plants scattered around the environment. In Indonesia, herbal plants thrive and are abundantly found and used as a concoction of traditional medicine known for its medicinal properties from generation to generation. In the current Z-generation era, children lack an understanding of the types of plants that benefit life. This study identifies and predicts the pattern of the leaf shape of herbal plants. Methods: The dataset used in this study used 15 types of herbal plants with 30 leaf data for each plant to obtain 450 data used. The extraction process uses the GLCM algorithm, and classification uses the K-NN algorithm. Result: The results carried out through the testing process in this study showed that the accuracy rate of the leaf pattern prediction process was 74% of the total 15 types of plants used. Value: Process of identifying and predicting leaf patterns of herbal plants can be applied using the K-NN classification algorithm combined with GLCM with the level of accuracy obtained.
Push-Up Detector Applications Using Quality Function Development and Anthropometry for Movement Error Detection Muzakir, Ari; Kusmindari, Christofora Desi
Scientific Journal of Informatics Vol 5, No 2 (2018): November 2018
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v5i2.16332

Abstract

Push-up is the simplest and most widely performed sport. Although simple, it also has a high risk of injury risk if done not in accordance with the rules. Push-up detector is a good push-up motion monitoring solution. In this way, nonstandard movements can be detected and corrected immediately. It has two motion sensors integrated with Arduino-based microcontroller. From this detector tool got the data of push-up result from sensor mounted. Sensor data will be displayed in the application in real-time. Quality function development is used to determine the criteria of the user. The sample data involved 200 participants who followed the testing of this tool and got 90% who can do the push-up correctly. Factors that affect the height, age, and weight. Tests conducted on adolescent boys aged 18-23 years. The results of this study is an application capable of monitoring each push-up movement to position in accordance with the provisions to minimize injuries resulting from movement errors.
Model Data Mining sebagai Prediksi Penyakit Hipertensi Kehamilan dengan Teknik Decision Tree Muzakir, Ari; Wulandari, Rika Anisa
Scientific Journal of Informatics Vol 3, No 1 (2016): May 2016
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v3i1.4610

Abstract

Prevalensi hipertensi pada wanita hamil terjadi sebanyak 1.062 kasus (12,7%). Dari 1062 kasus ibu hamil dengan hipertensi, ditemukan 125 kasus (11,8%) yang telah didiagnosis dengan hipertensi oleh tenaga kesehatan. RSIA YK Madira Palembang sebagai pusat kesehatan harus mengembangkan metode yang dapat memprediksi risiko tinggi ibu hamil dengan hipertensi dari data hasil pemeriksaan kehamilan. Dengan memanfaatkan sumber data yang terdiri dari data perawatan antenatal, diterapkan teknik data mining dengan algoritma decision tree C4.5, berdasarkan Knowledge Discovery in Database (KDD). Sehingga akan ditemukan pengetahuan, informasi, dan pola tersembunyi dari data pelayanan antenatal, yang merupakan prediksi hipertensi pada kehamilan. Metode yang digunakan yaitu Algoritma C4.5. Setelah mendapatkan decision tree dan rules yang dapat memprediksi penyakit hipertensi dalam kehamilan, dilakukan evaluasi dengan supplied test set menggunakan WEKA dihasilkan kesalahan (error) 7.3427% dan tingkat akurasi 92.6573%. Data training yang berjumlah 286 instances, hal ini menunjukkan bahwa terdapat 265 instances yang akurat dan 21 instances yang error atau prediksinya salah. 
Watermarking Techniques Using Least Significant Bit Algorithm for Digital Image Security Standard Solution- Based Android Muzakir, Ari; Habibi, Mailan
Scientific Journal of Informatics Vol 4, No 1 (2017): May 2017
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v4i1.7290

Abstract

Ease of deployment of digital image through the internet has positive and negative sides, especially for owners of the original digital image. The positive side of the ease of rapid deployment is the owner of that image deploys digital image files to various sites in the world address. While the downside is that if there is no copyright that serves as protector of the image it will be very easily recognized ownership by other parties. Watermarking is one solution to protect the copyright and know the results of the digital image. With Digital Image Watermarking, copyright resulting digital image will be protected through the insertion of additional information such as owner information and the authenticity of the digital image. The least significant bit (LSB) is one of the algorithm is simple and easy to understand. The results of the simulations carried out using android smartphone shows that the LSB watermarking technique is not able to be seen by naked human eye, meaning there is no significant difference in the image of the original files with images that have been inserted watermarking. The resulting image has dimensions of 640x480 with a bit depth of 32 bits. In addition, to determine the function of the ability of the device (smartphone) in processing the image using this application used black box testing. 
A Comparative Analysis of Classification Algorithms for Cyberbullying Crime Detection: An Experimental Study of Twitter Social Media in Indonesia Muzakir, Ari; Syaputra, Hadi; Panjaitan, Febriyanti
Scientific Journal of Informatics Vol 9, No 2 (2022): November 2022
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v9i2.35149

Abstract

Purpose: This research aims to identify content that contains cyberbullying on Twitter. We also conducted a comparative study of several classification algorithms, namely NB, DT, LR, and SVM. The dataset we use comes from Twitter data which is then manually labeled and validated by language experts. This study used 1065 data with a label distribution, namely 638 data with a non-bullying label and 427 with a bullying label.Methods: The weighting process for each word uses the bag of word (BOW) method, which uses three weighting features. The three-word vector weighting features used include unigram, bigram, and trigram. The experiment was conducted with two scenarios, namely testing to find the best accuracy value with the three features. The following scenario looks at the overall comparison of the algorithm's performance against all the features used.Result: The experimental results show that for the measurement of accuracy weighting based on features and algorithms, the SVM classification algorithm outperformed other algorithms with a percentage of 76%. Then for the weighting based on the average recall, the DT classification algorithm outperformed the other algorithms by an average of 76%. Another test for measuring overall performance (F-measure) based on accuracy and precision, the SVM classification algorithm, managed to outperform other algorithms with an F-measure of 82%.Value: Based on several experiments conducted, the SVM classification algorithm can detect words containing cyberbullying on social media.
Co-Authors Adha, Laili Adhie Tri Wahyudi Ahmad Syazili Alfian Egi Erlangga Andri Wijaya Anisa Putri Ch Desi Kusmindari Christofora Desi Kusmindari Dilla Apriliani Edi Supratman Edo Irawan Eksen Hidiansah Febriyanti Panjaitan Febriyanti Panjaitan Hadi Syaputra Hamim Tezar Rizandi Hutrianto Hutrianto Idrus Salim Ifran Wahyudi Kusmindari, Christofora Desi Ledy Armyoktora Mailan Habibi, Mailan Maria Ulfa Meigi Rahman Moh Hafiz Trisaputra Muhammad Ridho Kurniawan Muhammad Ridwan Nyimas Sopiah Poppy Indriani R.M Nasrul Halim Rahman, Meigi Rendy Jemi Sahputra Reni Laili Rika Anisa Wulandari, Rika Anisa SANTOSO SANTOSO Syaputra, Hadi Uci Suriani Usman Ependi Usman Ependi Usman Ependi Winda Wulandari Yuli Dwi Astanti