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All Journal Bianglala Informatika : Jurnal Komputer dan Informatika Akademi Bina Sarana Informatika Yogyakarta Jurnal Teknik Komputer AMIK BSI Jurnal Khatulistiwa Informatika Cakrawala : Jurnal Humaniora Bina Sarana Informatika Paradigma JITK (Jurnal Ilmu Pengetahuan dan Komputer) Indonesian Journal of Applied Informatics JISICOM (Journal of Information System, Infomatics and Computing) Journal of Information System, Applied, Management, Accounting and Research Jurnal Teknik Informatika dan Teknologi Informasi
Roida Pakpahan
Universitas Bina Sarana Informatika
Arts Humanities Computer Science & IT Decision Sciences, Operations Research & Management Social Sciences

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Analisa Penyalahgunaan Media Sosial untuk Penyebaran Cybercrime di Dunia Maya atau Cyberspace Yuni Fitriani; Roida Pakpahan
Cakrawala - Jurnal Humaniora Vol 20, No 1 (2020): Maret 2020
Publisher : Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/jc.v20i1.6446

Abstract

Progress was technology that can make it easier to access information we need. But in its development, theprogression of the technology was also used as the emergence of opportunities cyber crime.Where is the moment, abuse ofsocial media is facilitating the spread of cybercrime in the cyberspace, As the emergence of cases defamation, cases of hatespeech, cases of hoax, fraud cases pretended to be the buying and selling of online, Online prostitution and the case other cybercrime. In the year 2018 more than 50 percent of evil siber derived from social media especially facebook and twitters. The research results show that abuse social media to the spread of cybercrime it is still going until now. Where most of an offender cybercrime in the media social either intentionally or unintentionally will be charged by The law No. 11 year 2008 on information and electronic transaction (UU ITE). Keywords: Social Media, Cybercrime, UU ITE
Penerapan Literasi Digital dalam Aktivitas Pembelajaran Daring Mahasiswa Yuni Fitriani; Roida Pakpahan; Bambang Junadi; Handini Widyastuti
JISAMAR (Journal of Information System, Applied, Management, Accounting and Research) Vol 6 No 2 (2022): JISAMAR: May 2022
Publisher : Sekolah Tinggi Manajemen Informatika dan Komputer Jayakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52362/jisamar.v6i2.784

Abstract

Abstract: In following the development of information technology, each individual is also required to be able to understand the concept of digital literacy, because with the understanding and application of digital literacy, each individual can participate and adapt in today's digital era. Digital literacy is also a literacy that is in line with the demands of learning transformation in the world of education during the Covid-19 pandemic. The application of digital literacy in students' online learning activities in this study is seen from the characteristics of student activities in online learning which include learning spirit, literacy in technology, ability to communicate interpersonally, collaborate and self-study skills. This study uses qualitative descriptive methods. Based on the results of research that the application of digital literacy in student online learning activities is quite good. Where with the application of digital literacy, students are still passionate about learning even though learning is carried out online with various technological media and digital devices. Students also have the ability to digital literacy in terms of mastery of technological media that supports online learning, able to collaborate with other students and lecturers by using online communication media in the digital space and have self-study skills.
SISTEM INFORMASI APLIKASI PELAYANAN PANTI SOSIAL Roida Pakpahan; Yuni Fitriani; Mahdiyyah Mahdiyyah
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol 5 No 1 (2019): JITK Issue August 2019
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1128.721 KB) | DOI: 10.33480/jitk.v5i1.588

Abstract

Social institutions function to provide skills training to women who were previously commercial sex workers to have skills so they are able to find decent jobs or able to build independent businesses. At present the Harapan Mulia Social Institution has used an information system to manage the data of the assisted citizens but the use of its information system still has several disadvantages including; the program does not have a data search facility that has been fostered so that the search for data of the assisted citizens will take a long time, the certificate of the assisted citizens is not systemic so that the printed certificate will take a long time because the staff will manually retrieve the data again. This study aims to design information systems using software development using the waterfall method by adding data searches on applications and storing certificates in the system to speed up the work process of employees.
Analysis of the Effect of Vuca on Mental Health After the Covid-19 Pandemic Roida Pakpahan
Journal of Information System, Informatics and Computing Vol 6 No 2 (2022): JISICOM: December 2022
Publisher : Sekolah Tinggi Manajemen Informatika dan Komputer Jayakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52362/jisicom.v6i2.965

Abstract

In the VUCA era, mental health is a very important aspect that must be owned so that every individual remains productive and happy in the midst of uncertainty. The Covid-19 pandemic has made the VUCA condition even more real, making people directly feel the volatility, uncertainty, complexity and ambiguity (VUCA). World health organization (WHO) states mental health problems have increased height during the Covid-19 pandemic. This is because during the Covid-19 pandemic, many people have lost their jobs, sources of income and deep sorrow over the loss of loved ones. On the other side, Covid-19 carried out volality, changes in the order of life from conventional systems to digital systems. Digital transformation makes the intensity of VUCA increase. This study aims to analyze of the effect of VUCA on mental health after the Covid-19 pandemic. The research results show that VUCA affects people's mental health. Covid-19 is part of VUCA, uncertainty which causes uncertainty in the world economy and leaves deep scarring effects, making problem even more complex. Uncertainty is the main factor that triggers mental health problems. The tendency to perceive uncertainty negatively makes people vulnerable to experiencing mental health problems, depression and anxiety. VUCA creates a new paradigm, it requires spiritual intelligence and mental resilience, self-awareness and the ability to respond well to change in order to survive in the midst of uncertainty. Growth mindset and learning agility so that they are capable of facing various challenges in the VUCA Era, because true life is a learning.
ANALYSIS OF THE INFLUENCE OF FLEXING IN SOCIAL MEDIA ON COMMUNITY LIFE Roida Pakpahan; Donny Yoesgiantoro
Journal of Information System, Informatics and Computing Vol 7 No 1 (2023): JISICOM (June 2023)
Publisher : Sekolah Tinggi Manajemen Informatika dan Komputer Jayakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52362/jisicom.v7i1.1093

Abstract

Flexing or showing off is a new habit that society loves today. Technological factors also influence flexing behavior. We Are Social 2023 states that 167 million Indonesians are active in using social media. In general, flexing is done to legitimize social status through various uploads on TikTok, Instagram Facebook, YouTube and others. This study aims to analyze of the influence of flexing in social media on community life. The results of the study show that luxury content is a favorite of netizens. Even though the luxury that is displayed is not necessarily true. Flexing has a positive influence because flexing can be used for employee branding through linkedIn. Flexing marketing to achieve success in popularity, business and endorsement. However, overall flexing has a more negative influence . Flexing creates consumptive behavior and hedonism. The use of goods is no longer interpreted as fulfilling needs, but rather as a brand or symbol to indicate lifestyle and social status. Sometimes flexing is used as a method of deception. POLRI and related institutions are expected to be more informative, actively monitoring and countering flexing content that has the potential to harm the public. Intelligence is needed in managing information, in the era of information openness, people must be more proficient in social media, filter flexing content so they don't get trapped in FOMO. Actually, flexing is the right of each individual, but it should not reduce the values of life. Minimize flexing behavior by prioritizing achievement.
Pemodelan Prediktif Emisi CO2 Kendaraan Kanada: Studi Komparatif Neural Network dan Support Vector Machine Rifki Nur Hidayat Putra; Nindya Dwi Lestari; Dinda Aprillia; Sumanto Sumanto; Imam Budiawan; Roida Pakpahan
IJAI (Indonesian Journal of Applied Informatics) Vol 10, No 1 (2025)
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20961/ijai.v10i1.110736

Abstract

Abstrak : Sektor transportasi merupakan penyumbang emisi karbon dioksida (CO2) terbesar yang memperparah perubahan iklim. Penelitian ini bertujuan mengembangkan model prediktif yang akurat untuk memperkirakan emisi CO2 kendaraan dengan memanfaatkan pendekatan pembelajaran mesin. Dataset yang digunakan adalah data emisi kendaraan Kanada dari Kaggle. Metode yang diterapkan adalah Support Vector Machine (SVM) dan Neural Network untuk menganalisis pola kompleks dari berbagai parameter teknis kendaraan, seperti ukuran mesin, jumlah silinder, dan jenis transmisi. Hasil penelitian menunjukkan bahwa Neural Network secara konsisten unggul dibandingkan SVM dengan tingkat akurasi prediksi melebihi 90% dan nilai F1-score mencapai 0,831 untuk model SVM serta 0,954 untuk model Neural Network, yang menunjukkan kinerja klasifikasi yang kuat dan konsisten. Neural Network juga terbukti lebih baik dalam menangkap hubungan non-linier antara karakteristik kendaraan dan emisi CO2. Keberhasilan model ini membuka peluang pengembangan model prediktif yang lebih canggih serta dapat menjadi dasar bagi pembuat kebijakan dalam merancang regulasi emisi yang lebih akurat dan berbasis data.=====================================================Abstract :The transportation sector is the largest contributor to carbon dioxide (CO2) emissions that exacerbate climate change. This research aims to develop an accurate predictive model to estimate vehicle CO2 emissions by utilizing a machine learning approach. The dataset used is Canadian vehicle emissions data from Kaggle. The methods applied are Support Vector Machine (SVM) and Neural Network to analyze complex patterns of various vehicle technical parameters, such as engine size, number of cylinders, and transmission type. The results showed that the Neural Network consistently excelled over SVM with a prediction accuracy rate exceeding 90% and an F1-score value of 0.831 for the SVM model and 0.954 for the Neural Network model, indicating a strong and consistent classification performance. Neural networks have also been shown to be better at capturing the non-linear relationship between vehicle characteristics and CO2 emissions. The success of this model opens up opportunities for the development of more sophisticated predictive models and can serve as a basis for policymakers to design more accurate and data-driven emissions regulations.
Solusi Virtual Try-On Kacamata Berbasis AI dengan Integrasi Model Deep Learning untuk E-Commerce Fashion Arnata Nur Rasyid; Asmawati Asmawati; Widya Viona Septi Tanjung; Sumanto Sumanto; Imam Budiawan; Roida Pakpahan
IJAI (Indonesian Journal of Applied Informatics) Vol 10, No 1 (2025)
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20961/ijai.v10i1.110772

Abstract

Abstrak : Banyak pengguna menghadapi kesulitan dalam memilih kacamata secara daring karena tidak dapat memastikan apakah model tertentu sesuai dengan bentuk wajah mereka. Masalah ini sering menimbulkan ketidakpuasan pelanggan dan tingginya tingkat pengembalian produk. Penelitian ini bertujuan untuk mengembangkan solusi Virtual Try-On kacamata berbasis kecerdasan buatan (AI), yang mengintegrasikan model deep learning untuk menciptakan pengalaman belanja daring yang lebih interaktif dan personal. Sistem bekerja dengan mendeteksi bentuk wajah dari foto yang diunggah pengguna menggunakan model Face Shape Detection yang telah dilatih dan mencapai akurasi hingga 89% kemudian memberikan rekomendasi kacamata yang paling cocok berdasarkan sistem rekomendasi Rule-Based. Setelah pengguna memilih salah satu produk dari daftar tersebut, sistem memanfaatkan AI Nano Banana untuk menggabungkan citra wajah dan produk kacamata secara realistis. Teknologi utama yang digunakan meliputi EfficientNetB2 sebagai model CNN utama, InsightFace untuk deteksi wajah presisi tinggi, dan AdamW sebagai algoritma optimasi. Hasil pengujian menunjukkan bahwa sistem ini efektif dalam menghasilkan visualisasi try-on yang akurat dan memuaskan, serta berpotensi meningkatkan konversi penjualan di platform e-commerce fashion.====================================================Abstract : Many users experience difficulty in selecting eyeglasses online due to the inability to determine whether a particular model suits their facial shape. This issue often results in customer dissatisfaction and high product return rates. This study aims to develop an AI-based virtual try-on solution for eyeglasses by integrating deep learning models to create a more interactive and personalized online shopping experience. The system functions by detecting the user’s face shape from an uploaded photo using a pre-trained Face Shape Detection model that achieves an accuracy of up to 89%, followed by a rule-based recommendation system that suggests the most suitable eyeglass frames. Once the user selects a product from the recommended list, the system utilizes AI Nano Banana to realistically generate a composite image of the user's face wearing the selected eyeglasses. The core technologies implemented include EfficientNetB2 as the primary CNN model for visual feature extraction, InsightFace for high-precision face detection, and AdamW as the optimization algorithm. Experimental results demonstrate that the system effectively generates accurate and realistic try-on visualizations, which are not only satisfactory to users but also have the potential to increase sales conversion rates in fashion e-commerce platforms.
Analisis Perbandingan Algoritma Random Forest, SVM, dan Logistic Regression untuk Menentukan Model Terbaik Prediksi Penyakit Diabetes Alghifar Firgiawan; Fauzan Nawwir Andriansyah; Raihan Naufal Ramadhan; Sumanto Sumanto; Imam Budiawan; Roida Pakpahan
Jurnal Teknik Informatika dan Teknologi Informasi Vol. 5 No. 3 (2025): Desember: Jurnal Teknik Informatika dan Teknologi Informasi
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jutiti.v5i3.6213

Abstract

Diabetes is a chronic metabolic disorder characterized by elevated blood glucose levels caused by the body’s inability to produce or effectively respond to insulin. The increasing prevalence of diabetes in Indonesia requires accurate data-driven early detection systems to assist the diagnostic process. This study aims to compare the performance of three machine learning algorithms—Support Vector Machine (SVM), Random Forest, and Logistic Regression—in predicting diabetes disease based on patient clinical data. The dataset used was obtained from the Kaggle repository titled 100,000 Diabetes Clinical Dataset. The research process was conducted using the Orange Data Mining software through several stages, including data preprocessing, One-Hot Encoding transformation, model training, and evaluation using the 10-Fold Cross Validation method. The results show that the Random Forest algorithm achieved the best performance with an accuracy of 97.1%, followed by Logistic Regression at 96.0% and SVM at 92.3%. These findings indicate that ensemble-based methods such as Random Forest outperform others in producing stable and accurate predictions for diabetes diagnosis
Komparasi Algoritma Machine Learning (Random Forest, Gradient Boosting, dan Ada Boosting) untuk Prediksi Tingkat Penyakit Alzheimer Muhammad Raviansyah; Andika Amansyah; Farhan Fadhilah; Sumanto Sumanto; Imam Budiawan; Roida Pakpahan
Jurnal Teknik Informatika dan Teknologi Informasi Vol. 5 No. 3 (2025): Desember: Jurnal Teknik Informatika dan Teknologi Informasi
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jutiti.v5i3.6227

Abstract

Alzheimer’s disease is one of the most common forms of progressive dementia and has become a major global health challenge as the aging population continues to increase. Early detection of this disease is crucial to mitigate its social, economic, and health impacts. In this context, data-driven approaches using machine learning algorithms can be utilized to predict Alzheimer’s risk more accurately. This study aims to compare the performance of three ensemble learning algorithms—Gradient Boosting, Random Forest, and AdaBoost—in predicting the risk level of Alzheimer’s disease using the public Alzheimer’s Disease Dataset, which includes demographic, clinical, and lifestyle data. The research process involved several stages, including data preprocessing, splitting data into training and testing sets, model training using cross-validation, and performance evaluation based on accuracy, precision, recall, F1-score, and AUC metrics. The experimental results show that the Gradient Boosting algorithm achieved the best performance with an accuracy of 0.956, an F1-score of 0.956, and an AUC of 0.985, demonstrating its ability to capture complex non-linear relationships among features such as age, MMSE score, and lifestyle factors. Meanwhile, Random Forest and AdaBoost achieved competitive yet slightly lower performance. These findings indicate that ensemble boosting approaches, particularly Gradient Boosting, hold great potential for medical decision-support systems in the early detection of Alzheimer’s disease and can serve as a foundation for developing more accurate and adaptive predictive models in the future.
Analisis Pola Pergerakan dan Prediksi Harga Emas Menggunakan Regresi Linear serta Model Time Series ARIMA dan VAR Roni Saputra Pratama; Ryehan Alfiansyah; Prasetyo Adi Suwignyo; Sumanto Sumanto; Imam Budiawan; Roida Pakpahan
Jurnal Teknik Informatika dan Teknologi Informasi Vol. 5 No. 3 (2025): Desember: Jurnal Teknik Informatika dan Teknologi Informasi
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jutiti.v5i3.6233

Abstract

Gold is one of the most popular investment instruments due to its stable value and ability to protect assets against inflation. However, its price tends to fluctuate significantly, influenced by macroeconomic factors such as exchange rates, interest rates, and global geopolitical conditions. This study aims to analyze the movement patterns and predict gold prices based on historical data from 2019 to 2024 using the Linear Regression method and Time Series models, namely ARIMA and VAR. The analysis process was carried out using Orange Data Mining software, which enables the application of machine learning algorithms through a visual and interactive interface without manual coding. The dataset used consists of daily gold closing prices, processed and tested to evaluate model accuracy using Root Mean Square Error (RMSE) and Correlation Coefficient (R) indicators. The results indicate that the Linear Regression model effectively captures the general trend of gold prices, while ARIMA and VAR models produce more accurate forecasts based on historical fluctuations. The integration of regression and time series approaches improves prediction reliability. Overall, this research contributes to the development of financial data analysis and provides insights for investors in making more informed and data-driven investment decisions.
Co-Authors Adriyanichsan Adriyanichsan Ahcmad Anwar Asyirri Alghifar Firgiawan Alwan Kapi Muntaha Alya Avisa Andika Amansyah Arnata Nur Rasyid Asmawati Asmawati Asriani Asriani Astri Rezky Jumarah Audy Aulia Azzahra Bambang Junadi Cahyani Ayu Sulistyawati Desiana Nuranudin Putri Dinda Aprillia Erlangga Rizki Ekaptra Fajar Yoga Adiansyah Farhan Fadhilah Faris Syahrendra Fauzan Nawwir Andriansyah Ginting Wibi Prasetyo Handini Widyastuti Imam Budiawan Imam Wahyudi Indra Zaman Iqro Mukti Arto Joseph Melchior Nababan Kevin Dwi Satria Mahdiyyah Mahdiyyah Muhammad Raviansyah Nindya Dwi Lestari Nurul Afni Paulus Paulus Prasetyo Adi Suwignyo Putra Satria Raihan Naufal Ramadhan Rifki Nur Hidayat Putra Roni Saputra Pratama Ryehan Alfiansyah Sumanto, Sumanto Tarmidzi Ibrahim Vemi Januar Pratama Widya Viona Septi Tanjung Yoesgiantoro, Donny Yuni Fitriani Yuni Fitriani Yuni Fitriani Zahwa Asfa Rabbani