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All Journal Sistemasi: Jurnal Sistem Informasi IJCIT (Indonesian Journal on Computer and Information Technology) Indonesian Journal on Software Engineering (IJSE) Techno Nusa Mandiri : Journal of Computing and Information Technology Jurnal Manajemen Informatika dan Sistem Informasi Infotech: Jurnal Informatika & Teknologi Computer Science (CO-SCIENCE) Paradigma Prosiding Seminar Nasional Rekayasa dan Teknologi (TAU SNAR- TEK)
Hendri Mahmud Nawawi
Universitas Nusa Mandiri
Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering

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Analisis Pengaruh Intensitas Penggunaan Internet Terhadap Prestasi Belajar Siswa SMA Al-Mukrom Bojonggambir Fitra Septia Nugraha; Deddy Supriadi; Hendri Mahmud Nawawi; Ahmad Hafidzul Kahfi
IJCIT (Indonesian Journal on Computer and Information Technology) Vol 6, No 2 (2021): IJCIT November 2021
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (359.368 KB) | DOI: 10.31294/ijcit.v6i2.11918

Abstract

ABSTRAKSalah satu bentuk komunikasi yang paling banyak digunakan saat ini adalah internet. Berdasarkan hasil survei APJII menunjukan, pemanfaatan internet lebih digunakan sebagai gaya hidup. Peneliti juga melihat rendahnya minat peserta didik SMA Al-Mukrom Bojonggambir terhadap belajar dan lebih memilih memainkan handphone untuk berinternet. Penelitian ini bertujuan untuk mengetahui pengaruh intensitas penggunaan internet terhadap prestasi belajar siswa dan dampak intensitas penggunaan internet pada siswa SMA Al-Mukrom Bojonggambir. Penelitian ini merupakan penelitian survei. Variabel bebas yang digunakan adalah intensitas penggunaan internet dengan variabel terikatnya yaitu prestasi belajar. Subjek penelitian adalah siswa kelas XI angkatan 2018 dengan sampel 47 siswa. Data diperoleh dari hasil kuisioner dan dokumentasi. Teknik analisis data menggunakan statistik deskriptif dan analisis regresi. Uji validitas menggunakan korelasi product moment dan uji reliabilitas menggunakan rumus Cronbach Alpha. Uji prasyarat analisis menggunakan uji normalitas dan uji linieritas, sedangkan uji hipotesis menggunakan regresi linier sederhana. Hasil penelitian menunjukkan bahwa terdapat pengaruh negatif antara intensitas penggunaan internet terhadap prestasi belajar siswa. Hal ini dibuktikan dengan nilai koefisien korelasi sebesar -0,722 dan nilai koefisien determinasi (Rsquare) yang didapat sebesar 0,522. Persamaan regresi yang diperoleh yaitu Y=95,228+(-0,578)X. Intensitas penggunaan internet di kalangan siswa ini masuk kategori heavy user.ABSTRACTSOne of the most widely used forms of communication today is the internet. Based on the results of the APJII survey, the use of the internet is more used as a lifestyle. The researcher also saw the low interest of students at Al-Mukrom Bojonggambir High School towards learning and preferring to play their cellphones to surf the internet. This study aims to determine the effect of the intensity of internet use on student achievement and the impact of the intensity of internet use on students of Al-Mukrom Bojonggambir Senior High School. This research is a survey research. The independent variable used in this study is the intensity of internet use and the dependent variable is learning achievement. The research subjects were class XI 2018 students with a sample of 47. The data were obtained from the results of questionnaires and documentation. The data analysis technique used descriptive statistics and regression analysis. The validity test uses the product moment correlation and the reliability test uses the Cronbach Alpha formula. The analysis prerequisite test used normality test and linearity test, while the hypothesis test used simple linear regression. The results showed that there was a negative influence between the intensity of internet use on student achievement. This is evidenced by the correlation coefficient value of -0.722 and the coefficient of determination (Rsquare) obtained by 0.522. The regression equation obtained is Y=95,228+(-0,578)X. The intensity of internet use among students is in the heavy user category.
Sistem Pendukung Keputusan Pemilihan Tempat Usaha Potensial dengan Metode SAW (Studi Kasus : SahabatLink Tasikmalaya) Hendri Mahmud Nawawi; Yudhistira Yudhistira; Ali Mustopa; Siti Khotimatul Wildah; Sarifah Agustiani; Muhammad Iqbal
Indonesian Journal on Software Engineering (IJSE) Vol 7, No 1 (2021): IJSE 2021
Publisher : Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/ijse.v7i1.9990

Abstract

Memutuskan tempat untuk membuka usaha adalah hal yang sangat penting dan wajib diperhatikan saat akan memulai bisnis baru atau membuka cabang, sejumlah faktor penting diperhitungkan supaya dapat meminimalisir resiko kerugian di masa yang akan datang sehingga tujuan dari bisnis yaitu meningkatkan keuntungan bisa dicapai secara maksimal, pada penelitian ini sejumlah faktor dicatat dan dijadikan sebagai kriteria untuk menilai tempat usaha yang layak dan potensial berdasarkan hasil observasi dan pengamatan di lapangan pada tempat usaha dengan merk dagang SahabatLink dengan menggunakan metode Simple Additive Weight. Konsep metode SAW adalah mencari penjumlahan terbobot berdasarkan rating kinerja dari setiap alternatif yang ditambahkan dengan banyak kriteria.  Hal ini yang menjadikan metode ini tepat digunakan untuk menentukan keputusan memilih tempat usaha potensial dengan banyak kriteria diantaranya akses, visibilitas, lalu lintas,  persaingan, jarak ke tempat keramaian, tempat parkir, biaya sewa, ekspansi dan konduktivitas.  Hasil akhir dari penjumlahan kriteria inputan dengan metode SAW dapat menjadi rekomendasi bagi pihak manajemen untuk membuka tempat usaha berdasarkan nilai alternatif yang paling tinggi.
ALGORITMA C4.5 UNTUK MEMPREDIKSI PENGAMBILAN KEPUTUSAN MEMILIH DEPOSITO BERJANGKA Hendri Mahmud Nawawi; Sri Rahayu; Muhammad Ja’far Shidiq; Jajang Jaya Purnama
Jurnal Techno Nusa Mandiri Vol 16 No 1 (2019): Techno Nusa Mandiri : Journal of Computing and Information Technology Periode Ma
Publisher : Lembaga Penelitian dan Pengabdian Pada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1034.509 KB) | DOI: 10.33480/techno.v16i1.437

Abstract

Deposits are one form of investment offered by the Bank or other financial institutions with the nature of regulating and binding according to the rules set by the manager and the investor or commonly called investors. The advantage of being an investor is getting a fee or profit calculated based on the agreed time period at the beginning of the agreement. Whereas for investment fund managers can be used to advance and develop their business and business. Finding and determining potential customers is the first step to running a financial business in the form of this deposit, before the transaction decision is taken which is a favorable decision for both parties, investors or managers, one of the decision-making techniques can be done using Data Mining using the C4.5 Algorithm which is a structured decision-making technique based on input variables so that it can produce the most potential typical information for customers to participate in time deposits.
Feature Extraction With Forest Classifer To Predicate Covid 19 Based On Thorax X-Ray Results Ali Mustopa; Hendri Mahmud Nawawi; Sarifah Agustiani; Siti Khotimatul Wildah
Sistemasi: Jurnal Sistem Informasi Vol 11, No 2 (2022): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (171.973 KB) | DOI: 10.32520/stmsi.v11i2.1966

Abstract

Coronavirus 19 (COVID-19) is a highly contagious infection caused by the acute respiratory syndrome coronavirus 2 (SARS-CoV-2). COVID-19 is a new virus for which no cure has been found, marked by the increasing death rate worldwide. Coronavirus disease which can cause pneumonia which attacks the air sacs of the lungs with symptoms of dry cough, sore throat to acute respiratory distress (ARDS) that occurs in COVID-19 patients. One of the ways to detect the virus is by detecting chest X-rays in the patient. Over the past decade's mechine learning technology has developed rapidly and is integrated into CAD systems to provide accurate accuracy. This research was conducted by detecting thoracic radiographs using feature extraction Hu-Moments, Harralic and Histogram and detecting the best accuracy with a classification algorithm to detect the results of COVID-19. The study was conducted by testing the dataset obtained from the Kaggle repository which has images, namely 1281 X-rays of COVID-19, 3270 X-rays Normal, 1656 X-rays of  pneumonia, and X-rays of bacteria-pneumonia 3001. In general, this research is included in the Good category because it produces the highest accuracy by the Random forest classification algorithm where the accuracy result is 84% and the standard deviation is 0.015847. In addition, the research also produced Kappa of 0.713. The results of this accuracy are carried out in several stages, namely by feature extraction in the form of hu-moments, Harralic and histogram. In this study, the best results were given by the Random forest algorithm with feature extraction Histogram and Hu-Moment.
Analisis Tingkat Penerimaan Mahasiswa Terhadap Aplikasi Zoom Meeting Sebagai Media Perkuliahan Menggunakan Metode TAM Muhammad Ifan Rifani Ihsan; Rabiatus Saadah; Rizka Dahlia; Badariatul Lailiah; Hendri Mahmud Nawawi
Paradigma Vol. 24 No. 1 (2022): Periode Maret 2022
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (268.204 KB) | DOI: 10.31294/paradigma.v24i1.973

Abstract

The increasingly advanced technology makes it easier for anyone to use it for various activities, such as lecture activities. Especially during the Covid-19 pandemic that has occurred since 2019 until this research was conducted. Covid-19, formerly known as SARS-CoV2, is an outbreak of pneumonia that originates from a virus. This virus was first heard in Wuhan, China in December 2019 (Ciotti et al., 2020). Lectures are conducted online to prevent the spread of this virus. One of the technologies widely used during the Covid-19 pandemic is video conferencing. With video conference meetings can be held even if the people present are far from each other. Zoom is a cloud-based application that is currently often used as a video communication (Azkiya, 2021). Zoom is a video conferencing application that is currently being used in various activities, including lectures. Because it is important to know how much student acceptance of the Zoom application they use for lectures is. The research was conducted using the Technology Acceptance Model or TAM method which consists of three constructs, namely Perceived Usefulness, Perceived Ease of Use and Acceptance of Technology. The data was obtained by distributing questionnaires with a Likert scale of 1 to 5. The data obtained were then calculated using the Structural Equation Model or SEM method consisting of the Outer Model and Inner Model calculations. SEM is an analytical technique that allows testing of a series of simultaneous relationships (Gardenia, 2018). The conclusion is that by taking the R-Square value, the construct in the TAM method is able to measure as much as 63% of student admission case studies on Zoom as a lecture medium. Keywords: Analysis, TAM, Zoom.
OPTIMASI HYPERPARAMETER MULTILAYER PERCEPTRON UNTUK PREDIKSI DAYA BELI MOBIL Muhammad Iqbal; Hendri Mahmud Nawawi; M Rangga Ramadhan Saelan; Muhammad Sony Maulana; Yudhistira; Ali Mustopa
Jurnal Manajemen Informatika dan Sistem Informasi Vol. 6 No. 1 (2023): MISI Januari 2023
Publisher : LPPM STMIK Lombok

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36595/misi.v6i1.739

Abstract

Dalam memutuskan untuk membeli mobil biasanya beberapa faktor dijadikan pertimbangan untuk menentukan keputusan akhir. Maka dari itu sejumlah faktor pendukung seperti harga, type, merk, dan lain sebagainya penting untuk diperhatikan.  Pada penelitian ini optimasi model algoritma multilayer perceptron digunakan untuk memodelkan prediksi daya beli mobil konsumen dari dataset publik yang bersumber dari kaggle untuk menemukan model paling optimal terhadap keputusan membeli mobil.  Multilayer perceptron sering diterapkan untuk meneliti data yang kompleks karena mampu mengnalisa data dengan baik.  Prediksi niat beli tidak hanya dapat mengurangi biaya dealer mobil, tetapi juga mempengaruhi strategi pemasaran dealer mobil dalam jangka panjang. Pengujian menggunakan model Multilayer Perceptron (MLP) dengan konfigurasi default dan hypertuning parameter dilakukan dengan membandingkan dua parameter optimasi yang berbeda yaitu parameter Adam dan RMSprop.  Hasilnya didapatkan evaluasi optimal dari konfigurasi default pada parameter optimasi Adam dengan maksimum learning rate 0.01 dengan akurasi  89.50% dan 87,50% untuk optimasi RMSprop sedangkan pengujian dengan konfigurasi hyperparameter tuning dengan dua parameter optimasi yang sama Adam dan RMSprop dengan nilai maksimum learning rate 0,001 didapatkan akurasi sebesar 92.00% untuk parameter RMSprop dan 91,5% pada parameter Adam.
Pengenalan Wajah Menggunakan Pembelajaran Mesin Berdasarkan Ekstraksi Fitur Pada Gambar Wajah Berkualitas Rendah Siti Khotimatul Wildah; Sarifah Agustiani; Ali Mustopa; Nanik Wuryani; Hendri Mahmud Nawawi; Rizky Ade Safitri
INFOTECH : Jurnal Informatika & Teknologi Vol 2 No 2 (2021): INFOTECH: Jurnal Informatika & Teknologi
Publisher : LPPMPK - Sekolah Tinggi Teknologi Muhammadiyah Cileungsi

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (323.22 KB) | DOI: 10.37373/infotech.v2i2.189

Abstract

Wajah merupakan bagian dari sistem biometric dimana wajah manusia memiliki bentuk dan karakteristik yang berbeda antara satu dengan lainnya sehingga wajah dapat dijadikan sebagai alternatif pengamanan suatu sistem. Proses pengenalan wajah didasarkan pada proses pencocokan dan perbandingan citra yang dimasukan dengan citra yang telah tersimpan di database. Akan tetapi pengenalan wajah menjadi permasalahan yang cukup menantang dikarenakan illuminasi, pose dan ekspresi wajah serta kualitas citra. Oleh sebab itu pada penelitian ini bertujuan untuk melakukan pengenalan wajah dengan menggunakan metode machine learning seperti Logistic Regression (LR), Linear Discriminant Analysis (LDA), Decision Tree Classifier, Random Forest Classifier (RF), Gaussian NB, K Neighbors Classifier (KNN) dan Support Vector Machine (SVM) dan beberapa metode ekstraksi fitur Hu-Moment, HOG dan Haralick pada dataset Yale Face. Berdasarkan pengujian yang dilakukan metode ekstraksi fitur gabungan Hu-Moment, HOG dan Haralick dengan algoritma Linear Discriminant Analysis (LDA) menghasilkan nilai akurasi tertinggi sebesar 79,71% dibandingkan dengan metode ekstraksi fitur dan algoritma klasifikasi lainnya.
Pendekatan Algoritma Klasifikasi Machine Learning untuk Deteksi Penyakit Demensia Muhammad Iqbal; Hendri Mahmud Nawawi; Muhammad Rezki; Abdul Hamid; Sri Rahayu
Computer Science (CO-SCIENCE) Vol. 3 No. 2 (2023): Juli 2023
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/coscience.v3i2.1987

Abstract

Early detection of dementia through the use of machine learning classification algorithms is important for providing appropriate interventions to patients. In this context, this study aims to compare the performance of several machine learning classification algorithms in detecting dementia using the attribute selection method. In the early stages, patient data including medical history, cognitive test results, and other attributes were collected as input, an attribute selection algorithm was used to select the most informative attribute subset in detecting dementia. The subset of attributes used as input for training machine learning classification models, several classification algorithms such as Extra Trees (ET), Linear Discriminant Analysis (LDA), Random Forest (RF) and Ridge. In this study, accuracy is used as the main metric to compare algorithm performance. The evaluation results show that the Random Forest (RF) algorithm produces the best performance with an accuracy of 91.56%. The Extra Trees (ET) algorithm has an almost comparable accuracy of 91.44%, while Ridge and Linear Discriminant Analysis (LDA) have an accuracy of 90.44% respectively. In the context of dementia detection, the performance of the Random Forest algorithm with the attribute selection method proved to be the best with an accuracy of 91.56%. These results indicate that the developed model is capable of recognizing complex patterns and relationships between features that are relevant to dementia status. The use of the attribute selection method also contributes to increasing the accuracy and efficiency of the classification algorithm.
Algoritma Naïve Bayes Classifier Untuk Memprediksi Gejala Autism Spectrum Disorders Pada Anak-Anak Sri Rahayu; Jajang Jaya Purnama; Hendri Mahmud Nawawi; Fitria Septia Nugraha
Prosiding TAU SNARS-TEK Seminar Nasional Rekayasa dan Teknologi Vol. 1 No. 1 (2019): Prosiding TAU SNAR-TEK Seminar Nasional Rekayasa dan Teknologi 2019
Publisher : Fakultas Teknik dan Teknologi - TANRI ABENG UNIVERSITY

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

Abstract

Manusia merupakan makhluk sosial yang tidak bisa terlepas dari hubungan dengan manusia lainnya demi keberlangsungan hidup bersama, salah satunya dengan berkomunikasi. Dengan berkomunikasi, pikiran, perasaan dan kebutuhan seseorang dengan dunia luarnya dapat tersampaikan. Pengaruh besar ini dibuktikan oleh Peter Drucker seorang analis manajemen Amerika yang menilai bahwa di negara-negara yang sudah maju, maka setiap pembelanjaan dalam bentuk dollar, selain untuk makanan dan pakaian dihabiskan untuk kepentingan berkomunikasi. Melihat begitu pentingnya komunikasi ini, terlihat sangat disayangkan, masih saja ada beberapa orang yang terlahir dengan kesulitan dalam hal berkomunikasi, berinteraksi bahkan merespon interaksi dari orang lain akibat dari berbagai gangguan yang terjadi dalam organisme tubuhnya sejak ia dilahirkan. Salah satu disorders (gangguan) itu adalah disebut dengan Autism Spectrum Disorders (ASD), yaitu kelainan perkembangan saraf yang mempengaruhi perilaku dan cara seseorang berkomunikasi, mereka cenderung menghabiskan waktunya sendiri seperti memiliki dunia sendiri. para penderita Autism Spectrum Disorders (ASD) bisa ditolong dengan berbagai obat-obatan dan terapi yang dilakukan setelah diketahui gejala-gejala yang menunjukkan bahwa itu adalah gejala Autism Spectrum Disorders (ASD).Melihat kondisi seperti ini, penulis bermaksud untuk melakukan penelitian dengan melakukan klasifikasi terhadap geja-gejala Autism Spectrum Disorders (ASD),yang terjadi pada anak-anak. Dengan adanya klasifikasi ini diharapkan para orang tua lebih sigap dalam mengetahui kondisi kesehatan anaknya dari gangguan (disorders) yang mungkin saja terjadi, sehingga dapat langsung melakukan tindakan sebagai usaha untuk meminimalisir gejala yang terjadi dan diharapkan tindakan sejak dini. Klasifikasi dilakukan dengan sebuah algoritma Naïve Bayes yang dikenal merupakan salah satu model klasifikasi yang baik dan sering digunakan. Hasil dari penelitian ini mendapatkan accuracy sebesar 60,48% menggunakan rapid miner 9.1 dengan 248 dataset.
Chicken Disease Detection Based on Fases Image Using EfficientNetV2L Model Ali Mustopa; Agung Sasongko; Hendri Mahmud Nawawi; Siti Khotimatul Wildah; Sarifah Agustiani
Sistemasi: Jurnal Sistem Informasi Vol 12, No 3 (2023): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v12i3.2807

Abstract

Livestock farming requires technological innovation to increase productivity and efficiency. Chickens are a livestock animal with good market prospects. However, not all farmers understand about chicken diseases and signs of sickness. Detection of chicken diseases can be done through various methods, one of which is by looking at the shape of the chicken's feces. Images in feces can be detected using machine learning. Convolutional Neural Networks (CNN) are used to speed up disease prediction. Transfer learning is used to leverage knowledge that has been learned by previous models. In this study, we propose our own CNN architecture model and present research by building a new model to detect and classify diseases in chickens through their feces. The model training process is carried out by inputting training data and validation data, the number of epochs, and the created checkpointer object. The hyperparameter tuning stage is carried out to increase the accuracy rate of the model. The research is conducted by testing datasets obtained from the Kaggle repository which has images of coccidiosis, salmonella, Newcastle, and healthy feces. The results of the study show that our proposed model only achieves an accuracy rate of 93%, while the best accuracy rate in the study is achieved by using the EfficientNerV2L model with the RMSProp optimizer, which is 97%.
Co-Authors Abdul Hamid Agung Sasongko Agustiani, Sarifah Ahmad Hafidzul Kahfi Ali Mustopa, Ali Badariatul Lailiah Deddy Supriadi Fitra Septia Nugraha Fitria Septia Nugraha Jajang Jaya Purnama Laksono Trisnantoro M Rangga Ramadhan Saelan Muhammad Ifan Rifani Ihsan Muhammad Iqbal Muhammad Ja’far Shidiq Muhammad Rezki Muhammad Sony Maulana Nanik Wuryani Rabiatus Saadah Rizka Dahlia Rizky Ade Safitri Siti Khotimatul Wildah Sri Rahayu Sri Rahayu Sri Rahayu Yudhistira Yudhistira