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All Journal Panrita Abdi - Jurnal Pengabdian pada Masyarakat Sinkron : Jurnal dan Penelitian Teknik Informatika Tech-E JRST (Jurnal Riset Sains dan Teknologi) Jurnal Media Elektro : Journal of Electrical Power, Informatics, Telecommunication, Electronics, Computer and Control System Journal of Informatics and Communication Technology (JICT)
Arif Rahman Hakim
Universitas Medika Suherman
Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Mathematics Other

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 1 
PERAMALAN HARGA MATA UANG KRIPTO SOLANA MENGGUNAKAN METODE SUPPORT VECTOR REGRESSION (SVR) Dewi Marini Umi Atmaja; Arif Rahman Hakim
Jurnal Media Elektro Vol 11 No 2 (2022): Oktober 2022
Publisher : Universitas Nusa Cendana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35508/jme.v0i0.8117

Abstract

Cryptocurrencies have great potential to be adopted in Indonesia as an alternative to investing. One of the cryptocurrencies that investors or traders are interested in is Solana. Fluctuating price. movements make cryptocurrency investments considered. speculative so the risks. faced are very high. With this, we need a system or model that can help investors or traders to predict prices so that investors or traders have material for consideration in making decisions. The results of the descriptive analysis can be seen that in the period from April 10, 2020 to May 30, 2022, Solana's daily closing price movement fluctuates. The Support Vector Regression model obtained for Solana's daily closing price data, namely the Linear kernel with cost parameter C = 1000, obtained an. accuracy of 97.44% and MAPE 9.93 while for the Radial Basis Function. (RBF) kernel with cost parameter C = 1000 an .gamma = 0.1 obtained an accuracy of 87.76% with a MAPE value of 8.14. It can be concluded that through parameter tuning, the model formed has an accuracy value and the. best MAPE is to use a linear kernel with a cost parameter of C = 1000.
Android-Based Herpes Disease Detection Application using Image Processing Arif Rahman Hakim; Dewi Marini Umi Atmaja; Tugiman Tugiman; Amat Basri
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 1 (2023): Articles Research Volume 8 Issue 1, 2023
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i1.11913

Abstract

Herpes is a viral infection that causes a skin disease that is widespread throughout the world. Herpes virus is a DNA virus transmitted via infected skin, saliva, and other body fluids. Herpes is characterized by chickenpox-like nodules in one area of the skin, swollen tissue surrounding the nodule, and blister formation on the nodule. Digital image processing that can detect herpes disease is anticipated to reduce physical contact between physicians and patients during skin disease diagnosis. This study's methodology includes collecting data on herpes disease, developing machine-learning models using the CNN algorithm, and deploying the model as an Android application. This study makes use of actual data collected via smartphones, Pocket Cameras, and internet-sourced photographs. The data include 12,645 images of skin affected by herpes and normal skin. Using 100 epochs and the Adadelta optimizer, the accuracy of this study is 85 percent.
Implementation of Random Forest Algorithm on Palm Oil Price Data Arif Rahman Hakim; Dewi Marini Umi Atmaja; Amat Basri; Muhamad Syafii
Tech-E Vol. 6 No. 2 (2023): The Tech-E Journal Vol. 6 No. 2 publishes research papers in such informatics:
Publisher : Fakultas Sains dan Teknologi-Universitas Buddhi Dharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31253/te.v6i2.1757

Abstract

One of the potential commodities that are widely cultivated in Indonesia is palm oil, and palm oil or commonly referred to as palm oil is one of the processed products of palm oil which generates the most important foreign exchange for Indonesia. Data mining is a process that utilizes mathematical techniques, statistics, artificial intelligence, and machine learning techniques to extract and identify useful information and related knowledge from large databases [3], including palm oil price data. Random Forest is one of the methods in the decision tree. A decision tree is a flowchart shaped like a tree with a root node that is used to collect data that is used to solve problems and make decisions. In this study, a random forest algorithm was used to classify palm oil price data from 2014 to 2019. The classification method used the random forest algorithm on palm oil data using the Mtry parameter of 1 and the Ntree parameter of 500 resulting in an accuracy percentage of 100%. The most influential variable (importance variable) in the classification model using the resulting random forest algorithm is the palm oil variable.
Identifikasi Kebangkrutan Perusahaan Menggunakan Algoritma Regresi Linear Berganda Deny Haryadi; Arif Rahman Hakim; Dewi Marini Umi Atmaja; Amat Basri; Risma Adisty Nilasari
Tech-E Vol. 6 No. 2 (2023): The Tech-E Journal Vol. 6 No. 2 publishes research papers in such informatics:
Publisher : Fakultas Sains dan Teknologi-Universitas Buddhi Dharma

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Corporate bankruptcy can hurt the company and affect the state of the economy. Therefore, many interested parties want to know the business situation related to the company. These parties include creditors, auditors, shareholders, and management itself who have an interest in knowing the state of the company in the context of bankruptcy. The past financial statements of a company can be used to predict future financial conditions using report analysis techniques. In the risk assessment process, expert knowledge is still seen as an important task, because expert predictions are subjective. This study aims to predict the bankruptcy of the company using influencing factors such as the level of research and development costs, the growth rate of total assets, and the current asset turnover rate. The method used in this research is the prediction method using the Linear Regression Algorithm. Based on the test results show that the variables or attributes used in this study have a significant effect, as evidenced by using a linear regression algorithm to be able to produce a Root Mean Squared Error value: 0.162 +/- 0.000.
Prediction of Liver Disease Using a Linear Regression Algorithm Deny Haryadi; Dewi Marini Umi Atmaja; Arif Rahman Hakim
Journal of Informatics and Communication Technology (JICT) Vol 5 No 1
Publisher : PPM Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52661/j_ict.v5i1.182

Abstract

The liver is the most essential organ in the human body. Hepatitis is one such disorder affecting the liver and is a global health issue, including in Indonesia. Liver disease is an inflammatory condition of the liver that can be triggered by genetic factors, viral infections, alcohol consumption, and the use of certain drugs. In principle, prevention of hepatitis or liver disease can be done by adopting a healthy lifestyle. In addition, early detection is also very important in preventing death in those affected by this disease. One method for early detection is through the application of data mining, which can help predict and reduce mortality in patients affected by this disease. Linear regression is a data mining technique utilized to predict the dependent variable or outcome based on the independent variable or predictor. The study conducted tests on this algorithm and obtained a Root Mean Squared Error of 0.349 +/- 0.000. This indicates the presence of a correlation or functional relationship (cause and effect) between the dependent variable (criterion) and the independent variable (predictor). The purpose of this testing process is to detect liver disease using the linear regression algorithm.
Klasifikasi Metode Persalinan pada Ibu Hamil Menggunakan Algoritma Random Forest Berbasis Mobile Dewi Marini Umi Atmaja; Arif Rahman Hakim; Amat Basri; Andri Ariyanto
JRST (Jurnal Riset Sains dan Teknologi) Volume 7 No. 2 September 2023: JRST
Publisher : Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/jrst.v7i2.16705

Abstract

Tren angka kematian ibu pada saat melahirkan masih tinggi di Indonesia, yakni sekitar 300 per 100.000 kelahiran. Pemerintah Indonesia berencana untuk menurunkan angka tersebut menjadi 183 per 100.000 kelahiran pada tahun 2024 mendatang. Salah satu faktor penyebab kematian ibu hamil di Indonesia disebabkan oleh hipertensi dan terjadinya pendarahan pada saat melahirkan dan dibutuhkannya metode penanganan dalam persalinan. Adapun metode persalinan ibu hamil secara garis besar terbagi menjadi dua metode yaitu normal dan Caesar. Caesar adalah alternatif terakhir dalam persalinan, dikarenakan faktor risiko yang cukup tinggi, meskipun demikian, jumlah ibu yang menggunakan metode Caesar pada saat persalinan mengalami peningkatan yang cukup signifikan, khususnya di Indonesia. Metode persalinan pada ibu hamil dapat diklasifikasikan sesuai dengan kondisi ibu untuk menghindari risiko kematian ibu akibat pemilihan metode persalinan yang tidak tepat. Permasalahan tersebut dapat diselesaikan dengan memanfaatkan teknologi Machine Learning menggunakan algoritma random forest, dengan tujuan untuk membangun sebuah sistem yang dapat mengklasifikasi metode persalinan yang tepat berdasarkan kumpulan data persalinan ibu hamil yang telah disediakan. Dengan adanya sistem ini diharapkan dapat membantu para ibu hamil dalam melakukan screening awal untuk menentukan tindakan yang harus dilakukan agar proses persalinan berjalan dengan lancar dan meminimalisir risiko kematian ibu.
PELATIHAN PENGGUNAAN APLIKASI MOBILE UNTUK KLASIFIKASI METODE PERSALINAN PADA IBU HAMIL Arif Rahman Hakim
Panrita Abdi - Jurnal Pengabdian pada Masyarakat Vol. 8 No. 2 (2024): Jurnal Panrita Abdi - April 2024
Publisher : LP2M Universitas Hasanuddin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/pa.v8i2.25847

Abstract

The welfare of the people in a country can be assessed using several indicators, one of which is the minimum maternal mortality rate (MMR). The World Health Organization (WHO) records that an average of 810 women die every day due to complications related to pregnancy and childbirth. This figure is still quite high, and there must be serious handling to reduce it. The purpose of this service is to educate the public about the importance of choosing the right delivery method and provide training in using mobile-based delivery prediction applications (APRELIN). This application is expected to help the community avoid the risk of maternal death due to complications and choosing the wrong delivery method. The method used in this activity is in the form of workshops and direct practice of using the application. The partners who contributed to the implementation of the service were Medika Lestari Tangerang Hospital, which was attended by 30 people consisting of nurses, midwifery coordinators, IT staff, and general staff. This community service activity has been carried out well and successfully.   ||   Kesejahteraan masyarakat di suatu negara dapat dinilai dari beberapa indikator, salah satunya adalah minimnya angka kematian ibu (AKI). Organisasi Kesehatan Dunia (WHO) mencatat rata-rata 810 wanita meninggal setiap harinya akibat komplikasi terkait kehamilan dan persalinan. Angka tersebut masih cukup tinggi dan harus ada penanganan yang serius untuk menurunkannya. Tujuan pengabdian ini adalah untuk memberikan edukasi kepada masyarakat tentang pentingnya pemilihan metode persalinan yang tepat dan pelatihan penggunaan aplikasi prediksi persalinan (APRELIN) berbasis mobile. Aplikasi ini diharapkan dapat membantu masyarakat untuk menghindari risiko kematian ibu akibat komplikasi dan pemilihan metode persalinan yang tidak tepat. Metode yang digunakan dalam kegiatan ini berupa workshop dan praktik langsung penggunaan aplikasi. Adapun Mitra yang berkontribusi dalam pelaksanaan pengabdian adalah Rumah Sakit Medika Lestari Tangerang, yang dihadiri oleh 30 orang terdiri dari perawat, koordinator kebidanan, staff IT dan staff umum. Kegiatan pengabdian kepada masyarakat ini telah terlaksana dengan baik dan sukses.
Co-Authors Amat Basri Amat Basri Amat Basri Andri Ariyanto Deny Haryadi Dewi Marini Umi Atmaja Dewi Marini Umi Atmaja Dewi Marini Umi Atmaja Muhamad Syafii Risma Adisty Nilasari Tugiman Tugiman