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All Journal Jurnal Teknologi Informasi dan Ilmu Komputer Journal of Information Technology and Computer Science Knowledge Engineering and Data Science JOURNAL OF APPLIED INFORMATICS AND COMPUTING TEKTRIKA - Jurnal Penelitian dan Pengembangan Telekomunikasi, Kendali, Komputer, Elektrik, dan Elektronika Jurnal Ilmu Komputer dan Desain Komunikasi Visual Jurnal Mnemonic CICES (Cyberpreneurship Innovative and Creative Exact and Social Science) Jurnal Sistem Komputer dan Informatika (JSON) Community Development Journal: Jurnal Pengabdian Masyarakat Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Jurnal Teknik Informatika (JUTIF) Jurnal Restikom : Riset Teknik Informatika dan Komputer Nusantara Hasana Journal Jurnal Sistem Informasi Triguna Dharma (JURSI TGD) Jurnal Pengabdian Masyarakat Bhinneka Prosiding Seminar Nasional Pengabdian Kepada Masyarakat ROUTERS: Jurnal Sistem dan Teknologi Informasi Journal of World Future Medicine, Health and Nursing The Journal of Enhanced Studies in Informatics and Computer Applications Proceeding International Conference Of Innovation Science, Technology, Education, Children And Health Proceeding International Conference Of Innovation Science, Technology, Education, Children And Health
M. Syauqi Haris
Institut Teknologi, Sains, Dan Kesehatan RS Dr Soepraoen Kesdam V/Brw, Indonesia
Agriculture, Biological Sciences & Forestry Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Medicine & Pharmacology Nursing Public Health Social Sciences Veterinary Other

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Pelatihan Komputerisasi Keuangan Untuk Koperasi Syariah Ikmal Ponpes Al-Khoiroot Gondanglegi Malang Menggunakan Perangkat Lunak Akuntansi M. Syauqi Haris; Ahsanun Naseh Khudori; Wahyu Teja Kusuma; Nindynar Rikatsih; Mochammad Anshori
PROSIDING SEMINAR NASIONAL PENGABDIAN KEPADA MASYARAKAT UNIVERSITAS NAHDLATUL ULAMA SURABAYA Vol. 1 No. 1 (2022): Prosiding Seminar Nasional Pengabdian Kepada Masyarakat : Perguruan Tinggi Meng
Publisher : Universitas Nahdlatul Ulama Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1328.091 KB) | DOI: 10.33086/snpm.v1i1.877

Abstract

Pengelolaan keuangan pada koperasi secara manual akan menyulitkan dalam pembuatan laporan keuangan yang representatif secara tepat waktu. Selain karena kurangnya pemahaman SDM yang ada terhadap sistem akuntansi, pengarsipan dan pembukuan transaksi secara manual memerlukan waktu yang lama dalam inventarisasi atau rekap transaksi. Pemanfaatan teknologi informasi berupa perangkat lunak akuntansi dibutuhkan untuk memasukkan data setiap transaksi agar tersimpan secara digital sehingga dapat dilakukan pemrosesan secara otomatis dalam pembuatan laporan keuangan yang sesuai dengan prinsip akuntansi. Namun, implementasi perangkat lunak akuntansi harus diiringi dengan Standard Operating Procedure (SOP) yang sesuai dan jelas agar dalam operasional dan input transaksi bisa sesuai dan laporan yang dihasilkan sesuai dengan yang diharapkan. Oleh karena itu, proses diskusi untuk penggalian permasalahan transaksi dan pembuatan panduan operasional yang tepat perlu untuk disusun dan di-training-kan ke pengurus dan pengelola koperasi. Kegiatan ini diharapkan dapat meningkatkan SDM dari lembaga mitra kegiatan dalam hal pembuatan laporan keuangan secara akurat dan efisien dengan memanfaatkan teknologi informasi yang sudah terstandardisasi dengan prinsip akuntansi untuk entitas tanpa akuntabilitas publik (SAK-ETAP) yang menjadi dasar dalam penyusunan laporan koperasi. Berdasarkan hasil survey pasca kegiatan, 80% peserta menyatakan bahwa modul standar prosedur operasional atau SOP bagi SDM koperasi yang disusun sangat membantu dalam menjalankan aktivitas hariannya, terutama dalam melakukan input data transaksi ke dalam sistem. Selanjutnya proses pengawasan atau pendampingan secara berkala tetap diperlukan guna menjaga agar system tetap dijalankan dengan baik sesuai dengan SOP yang ada.
PREDICTION OF STUNTING PREVALENCE IN EAST JAVA PROVINCE WITH RANDOM FOREST ALGORITHM M. Syauqi Haris; Mochammad Anshori; Ahsanun Naseh Khudori
Jurnal Teknik Informatika (Jutif) Vol. 4 No. 1 (2023): JUTIF Volume 4, Number 1, February 2023
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2023.4.1.614

Abstract

Stunting or cases of failure to thrive in toddlers is one of the most serious health problems faced by the people of Indonesia. Based on data from the Ministry of Health and the Central Statistics Agency, East Java Province has a stunting prevalence value of 26.8% which is categorized as a high prevalence value according to the standards of the World Health Organization (WHO). Random forest is one of the machine learning algorithms in the field of artificial intelligence that can learn patterns from labeled data so that it can be used as a method for predicting or forecasting data. This approach is considered very suitable to be used in predicting the value of stunting prevalence because stunting prevalence data is usually accompanied by other data in the health sector according to survey results. Previous studies on the prediction of stunting prevalence used secondary data sourced from one survey only. Therefore, this study is one of the efforts to contribute in providing solutions for the stunting problem in East Java Province by combining several data from different surveys in the same year. The results of this study show that from 20 factor candidates for predicting stunting prevalence value, only 12 factors are suspected to be causative factors based on their correlation value. However, the prediction results obtained using the random forest algorithm in this study, with data consisting of 12 features and a dataset consisting of only 38 data, have results with error values of 1.02 in MAE and 1.64 in MSE that are not better than multi-linear regression which can produce smaller error values of 0.93 in MAE and 1.34 in MSE.
Penerapan Backpropagation Neural Network (BPNN) Untuk Prediksi Kecanduan Smartphone Pada Remaja Mochammad Anshori; M. Syauqi Haris; Wahyu Teja Kusuma
CICES (Cyberpreneurship Innovative and Creative Exact and Social Science) Vol 9 No 2 (2023): CICES
Publisher : UNIVERSITAS RAHARJA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/cices.v9i2.2701

Abstract

COVID-19 caused by the coronavirus disease-2019 has spread rapidly and attacked massively. As a precaution, a lockdown policy was issued. This policy limits the activities of schools, offices, shops, prohibits traveling at certain times, maintains distance from one another and reduces crowds in the public. During the lockdown period resulted in a new lifestyle where the use of smartphones increased. This increase is based on the fact that smartphones have many functions such as information, communication, education and entertainment. But excessive use of smartphones can cause addictive effects, especially in adolescents. Excessive use of smartphones makes teenagers become insomniac, different social behavior, low self-confidence, and even anxiety. The complexity of anxiety symptoms in adolescents tends to be difficult to understand, therefore a prediction of smartphone addiction with backpropagation is proposed. Parameter testing is done to get the right artificial neural network architecture. The results of testing the parameters that have been carried out are iterations = 50, the number of neurons in the hidden layer = 9 and the learning rate = 0.3. With this model, an accuracy of 99.49%, TPR of 99.5% and FPR of 0.08% is obtained. Keywords—Backpropagation, Artificial Neural Network, Smartphone Addiction, Machine Learning, Neural Network COVID-19 yang disebabkan oleh coronavirus disease-2019 telah menyebar dengan cepat dan menyerang secara masif. Sebagai tindakan pencegahan maka dikeluarkan kebijakan lockdown. Kebijakan ini membatasi kegiatan sekolah, perkantoran, pertokoan, melarang bepergian dalam waktu tertentu, saling menjaga jarak dan mengurangi kerumunan di publik. Selama masa lockdown menghasilkan gaya hidup yang baru dimana kegunaan smartphone meningkat. Peningkatan ini didasari karena smartphone memiliki banyak fungsi seperti informasi, komunikasi, edukasi dan hiburan. Tetapi penggunaan smartphone yang berlebihan dapat menimbulkan efek candu khususnya pada remaja. Berlebihan dalam menggunakan smartphone membuat anak remaja menjadi insomnia, Tingkah laku pergaulan yang berbeda, kepercayaan diri yang rendah, bahkan kecemasan. Kompleksnya gejala kecemasan pada anak remaja cenderung sulit untuk dipahami, oleh karena itu diusulkan prediksi kecanduan smartphone dengan backpropagation. Pengujian parameter dilakukan untuk mendapatkan arsitektur jaringan syaraf tiruan yang tepat. Hasil pengujian parameter yang telah dilakukan adalah iterasi = 50, jumlah neuron pada hidden layer = 9 dan nilai learning rate = 0.3. Dengan model tersebut, maka didapatkan akurasi sebesar 99.49%, TPR sebesar 99.5% dan FPR sebesar 0.08%. Kata Kunci—Backpropagation, Jaringan Saraf Tiruan, Kecanduan Smartphone, Pembelajaran Mesin, Jaringan Saraf
PREDIKSI PASIEN DENGAN PENYAKIT KARDIOVASKULAR MENGGUNAKAN RANDOM FOREST Mochammad Anshori; Nindynar Rikatsih; M. Syauqi Haris
TEKTRIKA Vol 7 No 2 (2022): TEKTRIKA Vol.7 No.2 2022
Publisher : Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25124/tektrika.v7i2.5279

Abstract

Cardiovascular disease is one of the deadliest diseases in the world. This is evidenced by data released by WHO which shows around 18 million deaths. This disease causes the cessation of the heartbeat which is the main source of life for the human body.This disease is caused by various things including an unhealthy lifestyle. Examples are consuming cigarettes and alcohol. In addition, it is also caused by other factors, namely health problems such as high blood pressure, cholesterol, diabetes, depression, or anxiety. The cardiovascular disease tends to be difficult to cure, therefore a precise and accurate prediction is needed in diagnosing patients. One method of making predictions is using machine learning techniques. In machine learning, there are various methods that can be used, one of which is the decision tree-based method, namely random forest. Before the random forest is implemented to create a model, the data is pre-processed by normalizing and applying cross-validation with k-fold = 10. The prediction results with the random forest in this study provide an accuracy of 98%. This accuracy is higher when compared to previous studies with the same dataset, namely 96.75% using the ensemble method and 91.61% with logistic regression. On this basis, it proves that the random forest can be used to predict cardiovascular disease. Key Words: cardiovascular disease, tree model, random forest, machine learning.
Predicting Heart Disease using Logistic Regression Mochammad Anshori; M. Syauqi Haris
Knowledge Engineering and Data Science Vol 5, No 2 (2022)
Publisher : Universitas Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17977/um018v5i22022p188-196

Abstract

A common risk of death is caused by heart disease. It is critical in the field of medicine to be able to diagnose cardiac disease in order to adequately prevent and treat patients. The most accurate method of prediction has the potential to both extend the patient's life and reduce the severity of their cardiac disease. The use of machine learning is one approach that may be taken to generate predictions. In this study, patient medical record information was used in conjunction with an algorithm for logistic regression in order to make heart disease diagnoses. The outcomes of the logistic regression have been utilized to achieve a high level of accuracy in the prediction of heart disease. To get the model coefficients needed for the equation, the experiment uses an iterative form of the logistic regression test. Iteration 14 produced the best results, with an accuracy of 81.3495% and an average calculation time of 0.020 seconds. The best iteration was reached at that point. The percentage of space that lies beneath the ROC curve is 89.36%. The findings of this study have significant implications for the field of heart disease prediction and can contribute to improved patient care and outcomes. Accurate predictions obtained through logistic regression can guide healthcare professionals in identifying individuals at risk and implementing preventive measures or tailored treatment plans. The computational efficiency of the model further enhances its applicability in real-time decision support systems.
Perancangan Audio Murottal Al-qur’an Untuk Terapi Emosi Anak Autis Menggunakan Metode Human Centered Design Wahyu Teja Kusuma; Faurika; M. Syauqi Haris; Ahsanun Naseh Khudori
Jurnal Ilmu Komputer dan Desain Komunikasi Visual Vol 8 No 1 (2023): Journal of Computer Science and Visual Communication Design
Publisher : Fakultas Ilmu Komputer Universitas Nahdlatul Ulama Sidoarjo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55732/jikdiskomvis.v8i1.901

Abstract

One of the characteristics of autistic children or autism spectrum disorder (ASD) is having emotional disturbances. This underlies the design of audio-visual murotal Al-Qur'an as a therapeutic tool for children with autism. Indirectly, murotal Al-Qur'an can calm children's emotions. This study aims to design practical audio-visual-based media for autistic children. The method in this study combines the Human Centered Design (HCD) method, persona, and expert validation. This research cycle begins with pre-production, production to the final result in the form of an audio-visual media design for the emotional therapy of autistic sufferers. The design of the audio-visual media was tested using the expert validation method with functional testing so that the results of the audio-visual media design from this study were by the requirements.
DISEMINASI SOCIAL MEDIA MARKETING BAGI COFFEE SHOP DI DESA TAMANSARI, LICIN, BANYUWANGI Mochammad Anshori; M. Syauqi Haris; Risqy Siwi Pradini
Community Development Journal : Jurnal Pengabdian Masyarakat Vol. 4 No. 4 (2023): Volume 4 Nomor 4 Tahun 2023
Publisher : Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/cdj.v4i4.18212

Abstract

Pengabdian masyarakat ini bertujuan untuk meningkatkan pengetahuan dan keterampilan para pemilik coffee shop di Tamansari, Banyuwangi, dalam menggunakan media sosial untuk pemasaran. Kegiatan ini dilakukan dengan memberikan pelatihan dan pendampingan dalam pembuatan dan pengelolaan akun media sosial, serta strategi pemasaran digital. Hasil kegiatan menunjukkan bahwa para pemilik coffee shop telah memiliki pemahaman yang baik tentang pentingnya media sosial untuk pemasaran. Namun, masih banyak yang perlu ditingkatkan dalam hal teknis pembuatan dan pengelolaan akun, serta strategi pemasaran digital. Melalui kegiatan ini, diharapkan para pemilik coffee shop dapat meningkatkan visibilitas dan daya saing bisnis mereka melalui media sosial. Berdasarkan hasil statistik dengan regresi linier terhadap pre dan pos tes, didapatkan nilai korelasi koefisien = 0,876457 yang berarti memiliki korelasi positif kuat. Presentase keberhasilan kegiatan ini sebesar 76,8% yang didapatkann dari nilai R square. Sedangkan nilai significance F = 0,000876, memiliki makna bahwa kegiatan diseminasi ini memiliki pengaruh secara signifikan karena nilai tersebut lebih dari batas ambang 0,05
Logistic Regression's Effectiveness in Feature Selection with Information Gain in Predicting Heart Failure Patients Mochammad Anshori; M. Syauqi Haris; Arif Wahyudi
Journal of Enhanced Studies in Informatics and Computer Applications Vol. 1 No. 2 (2024): JESICA Vol. 1 No. 2 2024
Publisher : Institut Teknologi, Sains, dan Kesehatan RS.DR. Soepraoen Kesdam V/BRW

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47794/jesica.v1i2.8

Abstract

Heart failure is a chronic illness that obstructs blood flow, which is necessary for the body to circulate oxygen. Patients with heart failure have a poor chance of survival, as evidenced by the high death rate. The hospital's infrastructure and medical facilities determine the degree of patient safety, and the patients' medical records play a significant role in ensuring that they receive the right care. As a result, a system that uses specific data to forecast the safety of heart failure patients is required. Machine learning, a computer-based approach, is one way to get around this. The logistic regression algorithm has been used to generate predictions in earlier studies. The approach for feature selection from the dataset that is suggested in this study is information gain. You can filter features that are significant to the dataset in this way. In addition, selection can enhance machine learning efficacy by decreasing the dimensions of the data. Five features—time, serum creatinine, ejection fraction, age, and serum sodium—are the outcome of information gain. After that, predictions were made using logistic regression, and a data sharing ratio of 70% training data and 30% test data resulted in an accuracy of 0.8556. This demonstrates how feature selection with Information Gain can improve the accuracy of the logistic regression model and is a very effective method.
Design of an Inventory Information System at ITSK Soepraoen Using the Waterfall Method Nugroho Teguh Yuono; M. Syauqi Haris; Risqy Siwi Pradini
Journal of Enhanced Studies in Informatics and Computer Applications Vol. 1 No. 2 (2024): JESICA Vol. 1 No. 2 2024
Publisher : Institut Teknologi, Sains, dan Kesehatan RS.DR. Soepraoen Kesdam V/BRW

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47794/jesica.v1i2.9

Abstract

In the current digital era, the use of information technology has become an urgent need for various institutions, including educational institutions. ITSK Soepraoen has integrated information systems in several units to increase operational efficiency. However, inventory data collection is still done manually using Microsoft Excel, which has proven to be less efficient and often causes delays in data processing. This research aims to design a website-based inventory information system at ITSK Soepraoen using the Waterfall method. This system is expected to facilitate data collection and management of inventory items as well as increase accuracy, transparency and efficiency in data processing. The research method used is the Waterfall approach which consists of four stages: requirements definition, system and software design, implementation, and testing. The result of this research is a lo-fi mockup of an inventory system that is well received by users with an acceptance rate of 92.75%. This percentage is relatively high so it can be concluded that the user accepts the design that has been created and for the next stage this inventory system can be fully implemented.
THE DISCRIMINANT ANALYSIS FUNCTION WAS IMPLEMENTED TO PREDICT THE PRESENCE OF DIABETES Herry Prasetyo Wibowo; Mochammad Anshori; M. Syauqi Haris
Journal of Enhanced Studies in Informatics and Computer Applications Vol. 1 No. 2 (2024): JESICA Vol. 1 No. 2 2024
Publisher : Institut Teknologi, Sains, dan Kesehatan RS.DR. Soepraoen Kesdam V/BRW

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47794/jesica.v1i2.10

Abstract

Diabetes is a condition blood sugar concentrations are high and there is something wrong with insulin inside the body. A hormone called insulin controls the equilibrium of blood sugar concentration in humans. Diabetes has high-risk health, such as CKD, CVD, skin disease or even blindness. The reason people suffer from diabetes is caused of bad consumption habits. Some symptoms of diabetes are frequent urination and feeling hungry too quickly. Diabetes is sometimes difficult to diagnose, which is why it is also referred to as the silent killer. A preventive way is an early prediction of diabetes disease. This is very important to do. In this study, the discriminant analysis algorithm is used along with machine learning techniques. In this study, machine learning techniques are used. Its name is discriminant analysis algorithm. Two popular versions are linear discriminant analysis (LDA) and quadratic discriminant analysis (QDA). This method is used because it is suitable for high-dimensional data and the discriminant analysis algorithm has minimal parameters. The discriminant analysis algorithm uses few parameters and this method appropriate for high-dimensional data. We'll compare the two approaches to find a way to demonstrate their dependability. Both approaches would be contrasted. Based on the result, QDA has the best performance. QDA can produce accuracy = 93.7%, TPR = 93.7%, precision = 94.3%, recall = 93.7% and F-measure = 93.9%. FPR of QDA is the lowest one, it is 1.02%. It means QDA has a small error in making predictions. Overall, based on the result QDA is the proven and proper method for detecting diabetes disease
Co-Authors Ahsanun Naseh Khudori Anwar, David Saiful Arif Wahyudi Bagus Prasetyo Abdi , Benben Basmalah Abirestu Maulisuriandhy Fatwa Ramdani, Fatwa Faurika Faurika, F Faurika, Faurika Ferdy Riza Firahmi Rizky Fita Rusdian Ikawati Geraldo, Tostes Herry Prasetyo Wibowo Hevlie Winda Nazry S Khofida, Nisa Marilia, Busatto Mika Debora Br Barus Mochamad Anshori Nindynar Rikatsih Nindynar Rikatsih Nugroho Teguh Yuono Risqy Siwi Pradini Risqy Siwi Pradini Saletia, Maria Claudia Tri Astoto Kurniawan, Tri Astoto Wahyu Tejakusuma, Wahyu Zuli Agustina Gultom