Article Per Year (5 Year)
p-Index From 2020 - 2025
1.116
P-Index
This Author published in this journals
All Journal BERKALA SAINSTEK Sistemasi: Jurnal Sistem Informasi Jurnal Pilar Nusa Mandiri Journal of Informatics Development
Hizham, Fadhel Akhmad
Unknown Affiliation
Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Electrical & Electronics Engineering Mathematics Physics

Published : 7 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 7 Documents
Search

 1 
Implementasi Metode Backpropagation Neural Network (BNN) dalam Sistem Klasifikasi Ketepatan Waktu Kelulusan Mahasiswa (Studi Kasus: Program Studi Sistem Informasi Universitas Jember) Hizham, Fadhel Akhmad; Nurdiansyah, Yanuar; Firmansyah, Diksy Media
BERKALA SAINSTEK Vol 6 No 2 (2018)
Publisher : Universitas Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.19184/bst.v6i2.9254

Abstract

Program Studi Sistem Informasi adalah salah satu program studi di Universitas Jember yang berdiri sejak tahun 2009. Sampai saat ini sudah cukup banyak mahasiswa yang telah menyandang gelar sarjana, khususnya angkatan 2009-2013 , namun tidak banyak yang berhasil menyelesaikan studinya tepat waktu sehingga berdampak pada penilaian akreditasi dari program studi tersebut. Mahasiswa memiliki beban pembelajaran sekurang-kurangnya 144 SKS dengan masa studi selama 4- 5 tahun untuk memperoleh gelar sarjana. Berdasarkan permasalahan tersebut, terdapat berbagai cara untuk mengklasifikasi ketepatan waktu kelulusan mahasiswa, salah satunya dengan metode jaringan syaraf tiruan Backpropagation. Data yang digunakan yaitu data lulusan mahasiswa Program Studi Sistem Informasi Universitas Jember angkatan tahun 2011-2013. Atribut yang digunakan untuk klasifikasi berjumlah 9 atribut, yaitu nilai Indeks Prestasi (IP) semester 1 sampai 6, jumlah SKS yang ditempuh, semester saat terakhir kali memprogram matakuliah Kuliah Kerja Nyata (KKN) dan Praktik Kerja Lapang (PKL). Kelas yang digunakan untuk klasifikasi yaitu ketepatan waktu lulus mahasiswa tersebut. Penentuan ketepatan waktunya yaitu jika masa studi kurang dari sama dengan 60 bulan, maka mahasiswa tersebut lulus tepat waktu dan jika lebih dari 60 bulan maka tidak tepat waktu. Penerapan metode klasifikasi ini dilakukan dengan menggunakan learning rate 0.1, 0.3, 0.5, 0.7, dan 0.9 dengan batas iterasi masing-masing 1.000, 2.000, dan 3.000 iterasi. Nilai akurasi tertinggi yaitu sebesar 98,82% pada iterasi ke-2000 dan 3000, masing-masing dengan learning rate = 0,7 dan 0,9 untuk iterasi ke-2000 dan learning rate = 0,5, 0,7 dan 0,9 untuk iterasi ke-3000. Hasil tersebut didapat dari jumlah data benar sebanyak 167 data dari 169 data secara keseluruhan. Kata Kunci: Data Mining, Klasifikasi, Jaringan Syaraf Tiruan, Metode Backpropagation Neural Network.
Sentiment Analysis of Ijen Crater Reviews using Decision Tree Classification and Oversampling Optimization Hizham, Fadhel Akhmad; Asyari, Hasyim; Urrochman, Maysas Yafi
Journal of Informatics Development Vol. 3 No. 1 (2024): Oktober 2024
Publisher : Institut Teknologi dan Bisnis Widya Gama Lumajang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30741/jid.v3i1.1399

Abstract

Sentiment analysis is a text mining technique that classifies content as positive, negative, or neutral polarity in each sentence or document. These lines or papers may be user reviews assessing the quality of a product or material supplied to them. The purpose of this study is to better understand the function of sentiment analysis in assessing evaluations of the Ijen Crater tourist destination based on Google Maps user comments. This study is conducted in four steps, beginning with data gathering in the form of Google Maps evaluations obtained by data scraping. Following data collection, text preparation includes case folding, tokenization, stopword elimination, and stemming. Following text preprocessing, the next stage is imbalaced data optimization, which involves modifying the minority class samples to be nearly equal to the majority class by randomly duplicating minority class samples. Then, each review is categorized according to sentiment using the Decision Tree (DT) method. Testing has done by comparing DT without optimization and DT with SMOTE-ENN and ADASYN optimization. The result shown DT with SMOTE-ENN optimization has the best accuracy improvement with 1.62%, from 96.94% to 98.56%.
PERFORMANCE COMPARISON OF RANDOM FOREST REGRESSION, SVR MODELS IN STOCK PRICE PREDICTION Urrochman, Maysas Yafi; Asy'ari, Hasyim; Hizham, Fadhel Akhmad
Jurnal Pilar Nusa Mandiri Vol. 21 No. 1 (2025): Pilar Nusa Mandiri : Journal of Computing and Information System Publishing Pe
Publisher : LPPM Universitas Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/pilar.v21i1.6072

Abstract

The stock market is characterized by high volatility and complexity, making it an intriguing and challenging subject for researchers and practitioners. This study aims to predict stock prices by comparing the performance of two machine learning models: Random Forest Regression and Support Vector Regression (SVR). These models were selected for their ability to handle complex data and high volatility. The dataset used in this study consists of BNI stock data over the last five years (2019–2024), comprising a total of 1,211 data points. Testing was conducted using a cross-validation approach, and model performance was evaluated based on several metrics, including MSE, R², RMSE, MAPE, MAE, and Score. The results indicate that Random Forest Regression outperforms SVR. The model achieved an MAE of 17.766, an RMSE of 22.376, and an R² of 0.997. These findings suggest that Random Forest Regression is more effective in predicting stock prices, particularly in unstable market conditions. This study recommends Random Forest Regression as a reliable model for stock price prediction, with potential applications in other stock markets with similar characteristics.
GENERATION Z'S AND ANDROID OS: HOW USER EXPERIENCE, SECURITY, AND SYSTEM PERFORMANCE SHAPE SATISFACTION Murni, Cahyasari Kartika; Choiri, Achmad Firman; Hizham, Fadhel Akhmad
Jurnal Pilar Nusa Mandiri Vol. 21 No. 1 (2025): Pilar Nusa Mandiri : Journal of Computing and Information System Publishing Pe
Publisher : LPPM Universitas Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/pilar.v21i1.6082

Abstract

This paper investigates Generation Z's opinion of the Android OS through an analysis of system performance, security, and user experience and their influence on general satisfaction. With Technology Adaptation (Z) as a mediator, the study focuses on three main factors: User Experience (X1), Security and Privacy (X2), and System Performance and Stability (X3), specifically among students who use Android devices. Data were collected through a structured questionnaire, and the analysis was conducted using the Partial Least Squares (PLS) method to evaluate the relationships between the variables. The findings reveal that, in addition to frequent updates, Generation Z's satisfaction is significantly influenced by the accessibility, performance, and security features of Android. The results highlight the importance of a positive user experience and robust security measures in enhancing user satisfaction. Continuous development in these areas is crucial for improving user engagement and contentment with Android devices.
Analysis and Visualization of Data on the Impacts of Covid-19 Globally and Locally Iqbal, Muhammad; Yudha, Julius Chaezar Bernard Buana; Umimah, Reza Nazilatul; Hizham, Fadhel Akhmad
Journal of Informatics Development Vol. 3 No. 2 (2025): April 2025
Publisher : Institut Teknologi dan Bisnis Widya Gama Lumajang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30741/jid.v3i2.1513

Abstract

The COVID-19 pandemic has had a profound impact on multiple aspects of human life, including food supply, mental health, and healthcare service management. This study aims to examine these impacts by applying a combination of data analysis methods such as data preprocessing, exploratory data analysis (EDA), predictive algorithms, and data visualization. The datasets utilized include information related to mental health conditions, food security, and COVID-19-related health statistics. The findings indicate a significant increase in mental health issues, such as anxiety and depression, as well as disruptions in food supply chains that have adversely affected global food security. Moreover, data visualization has proven to be a valuable tool in supporting decision-making processes in healthcare management. However, most implementations remain limited in scope and are often confined to internal agency use. Therefore, this study recommends further development in integrating data sources, enhancing the application of predictive algorithms, and optimizing data visualization for more effective decision-making in managing global health crises.
Analisis Sentimen Ulasan Kawah Ijen Menggunakan Naïve Bayes Classification dan Optimasi Oversampling Hizham, Fadhel Akhmad; Asy'ari, Hasyim; Urrochman, Maysas Yafi
Sistemasi: Jurnal Sistem Informasi Vol 13, No 5 (2024): 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.v13i5.4490

Abstract

Sentiment analysis is a method that applies text mining concepts to provide classifications that have polarity that is positive, negative, or neutral from each sentence or document. In this context, the purpose of this research is to analyse the sentiment of user reviews related to the Ijen crater tourist attractions found on the Google Maps platform. This research is conducted in three main stages: first, Data Collection and Preprocessing by taking data samples obtained from Ijen Crater reviews contained on Google Maps; second Optimisation and Classification by changing the minority class samples to be almost equal to the majority class by randomly duplicating the minority class samples, third, classification performance measurement using confusion matrix. The test is conducted by comparing the performance between NBC classification without optimisation and NBC classification with SMOTE and ADASYN optimisation. The performance results show that SMOTE-optimised NBC classification provides the best improvement in accuracy by 6.74% compared to the performance of ordinary NBC and NBC added with ADASYN.
Pengembangan Metode Information Retrieval dan Haversine Formula untuk Rekomendasi Penentuan Klinik di Kabupaten Jember Hizham, Fadhel Akhmad; Ginardi, Raden Venantius Hari
Journal of Informatics Development Vol. 1 No. 1 (2022): Oktober 2022
Publisher : Institut Teknologi dan Bisnis Widya Gama Lumajang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30741/jid.v1i1.896

Abstract

Klinik merupakan fasilitas tempat orang berobat dan memperoleh advis medis serta tempat mahasiswa kedokteran melakukan pengamatan terhadap kasus penyakit yang diderita para pasien. Saat ini, hadirnya virus Corona (COVID-19) membuat banyak klinik menampung pasien yang terpapar virus tersebut. Dari kasus tersebut, rekomendasi penentuan klinik sangat diperlukan karena kondisi yang sangat darurat dan kasus positif yang bertambah setiap harinya. Pada penelitian ini, ditambahkan metode information retrieval, yaitu metode TF-IDF dan BM25 untuk menentukan rekomendasi klinik di Kabupaten Jember berdasarkan kata pencarian dari penggunanya dan diurutkan berdasarkan kemiripan (similarity) dari yang terbesar hingga yang terkecil. Sementara metode Haversine Formula digunakan untuk memilih klinik dengan jarak yang ditentukan oleh pengguna sebelumnya Penentuan rekomendasi klinik yang menggunakan metode gabungan information retrieval (similarity) + haversine dilakukan dengan formulasi rata-rata peringkat antara metode haversine dengan metode gabungan, dan formulasi normalisasi nilai similarity maupun nilai haversine. Hasilnya, ada 7 klinik yang menempati peringkat terbaik untuk metode gabungan dengan formulasi rata-rata peringkat, dan ada 47 klinik yang menempati peringkat terbaik untuk metode gabungan dengan formulasi normalisasi.
Co-Authors Asy'ari, Hasyim Choiri, Achmad Firman Firmansyah, Diksy Media Ginardi, R.V. Hari Hasyim Asyari Muhammad Iqbal Murni, Cahyasari Kartika Umimah, Reza Nazilatul Urrochman, Maysas Yafi Yanuar Nurdiansyah, Yanuar Yudha, Julius Chaezar Bernard Buana