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All Journal Health Information : Jurnal Penelitian JENGGALA: Jurnal Riset Pengembangan dan Pelayanan Kesehatan Journal of Scientific Insights Jurnal Teknologi dan Sains Modern
Atnang, Muhammad
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Agriculture, Biological Sciences & Forestry Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Education Engineering Health Professions Medicine & Pharmacology Nursing Public Health Social Sciences Other

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LITERATUR REVIEW: PENERAPAN REKAYASA PERANGKAT LUNAK Ferdiansyah; Atnang, Muhammad
Jenggala : Jurnal Riset Pengembangan dan Pelayanan Kesehatan Vol 3 No 1 (2024): JUNI 2024
Publisher : Fakultas Teknologi dan Manajemen Kesehatan

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Abstract

Perkembangan teknologi dijaman sekarang ini sangatlah canggih dan pesat. Hal ini dapat dibuktikan dengan banyaknya inovasi dimasa ini, dengan yang sederhana maupun yang menghebohkan dunia, begitu juga dengan pengembangan perangkat lunak sebelumnya telah melihatkan hubungan antara kebutuhan dan arsitektur.Metode:Metodologi yang digunakan dalam penelitian ini adalah studi literatur.Hasil Penelitian: Rekayasa perangkat lunak sudah berfungsi dengan baik dan membantu program-program pemerintah atau Masyarakat yang ingin melaksanakan suatu kegiatan atau program-program yang di keluarkan yang berbasis perangkat lunak,namun pemenuhan standar kompetensi dalam penentuan mata kuliah dan kurikulum AMIK Indonesia menjadi lambat seiring dengan perkembangan rekayasa perangkat lunak.
REKAYASA PERANGKAT LUNAK INFORMASI KEMISKINAN: REKAYASA PERANGKAT LUNAK INFORMASI KEMISKINAN riskyawan; Atnang, Muhammad
Jenggala : Jurnal Riset Pengembangan dan Pelayanan Kesehatan Vol 3 No 1 (2024): JUNI 2024
Publisher : Fakultas Teknologi dan Manajemen Kesehatan

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Abstract

Rekayasa Perangkat Lunak Informasi (RPLI) muncul sebagai pendekatan strategis dalam menanggapi kompleksitas tantangan penanggulangan kemiskinan di era digital ini. Abstrak ini menjelaskan urgensi dan potensi perangkat lunak informasi dalam membantu merumuskan solusi-solusi inovatif untuk mengatasi masalah kemiskinan. Melalui pendekatan Rekayasa Perangkat Lunak (RPL), penelitian ini fokus pada pengembangan solusi yang sistematis dan terukur, menggabungkan pemahaman mendalam terhadap kondisi kemiskinan dengan kekuatan teknologi informasi. Penelitian ini merinci konsep desain perangkat lunak yang dapat memberdayakan masyarakat, meningkatkan efisiensi administrasi, dan menyediakan akses yang lebih baik terhadap sumber daya kritis. Dengan mempertimbangkan kompleksitas masalah sosial dan ekonomi yang terkait dengan kemiskinan, RPLI bertujuan untuk menciptakan solusi yang berkelanjutan, adaptif, dan dapat diimplementasikan secara luas. Hasil penelitian ini diharapkan dapat memberikan landasan bagi pengembangan perangkat lunak yang tidak hanya memberikan dampak langsung dalam mengurangi kemiskinan, tetapi juga mendukung pemberdayaan masyarakat dan peningkatan kualitas hidup. Pemanfaatan teknologi informasi dalam konteks penanggulangan kemiskinan bukan hanya sekadar implementasi solusi teknologis, tetapi juga merupakan upaya integral dalam mewujudkan inklusivitas dan keadilan sosial. Keseluruhan, penelitian ini berkontribusi pada pemahaman mendalam mengenai peran vital RPLI sebagai instrumen strategis dalam upaya global untuk mengentaskan kemiskinan.
Transforming the Diabetes Mellitus Diagnosis and Treatment Using Data Technology: Comprehensive Analysis of Deep Learning and Machine Learning Methodologies Anggriani, Dwi; Mustamin, Syaiful Bachri; Sahriani; Atnang, Muhammad; Fatmah, Siti; Mar, Nur Azaliah; Fajar, Nurhikmah
Journal of Scientific Insights Vol. 1 No. 1 (2024): June
Publisher : Science Tech Group

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69930/jsi.v1i1.71

Abstract

Recent research in health data analysis has transformed our understanding, prediction, and management of diabetes mellitus. This review explores various approaches used in related studies to enhance understanding and management strategies of diabetes through data analysis. Various data analysis methods, including machine learning such as neural networks, Gaussian Process Classification (GPC), and deep learning, have been used to enhance illness management and forecast accuracy. One of the included studies created customised care plans and used data to forecast the likelihood of complications in diabetes.. Another focused on comparative approaches for diabetes diagnosis using artificial intelligence, while others explored disease classification techniques using GPC algorithms. On the other hand, some studies utilized deep learning to identify diverse trajectories of type 2 diabetes from routine medical records, while others developed wide and deep learning models to predict diabetes onset. This review notes that data analysis approaches have significantly advanced accuracy in diagnosis, predictive modeling, and disease management of diabetes. Integrating these technologies allows for more personalized treatment approaches, where patient data can tailor individualized care strategies. Study findings indicate that machine learning and deep learning applications not only enhance prediction accuracy but also unlock new potentials in identifying risk factors, managing complications, and preventing diseases. Thus, this review provides profound insights into how data analysis has shifted paradigms in diabetes management, extending beyond diagnosis and treatment to encompass prevention and long-term management of chronic diseases. These studies lay a robust foundation for further research in developing more sophisticated and effective approaches in health data analysis, ultimately aiming to enhance the overall quality of life for patients with diabetes.
Research Techniques for IoT Use, Wearable Technology, and Smart Sensors in Mental Well-Being: A Literature Review from Several Studies Sahriani; Surahmawanti, Mita; Samsidar; Fatmah, Siti; Mustamin, Syaiful Bachri; Atnang, Muhammad; Fajar, Nurhikmah; Mar, Nur Azaliah
Journal of Scientific Insights Vol. 1 No. 1 (2024): June
Publisher : Science Tech Group

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69930/jsi.v1i1.72

Abstract

This study reviews the literature on the application of technology to wearables, smart sensors, and the Internet of Things (IoT) in the monitoring and treatment of mental health. Several studies analyzed employ systematic review, experimental, and literature survey approaches to explore various aspects of technology implementation in the context of mental health. The studies adopt a systematic review design without involving specific samples or measurement tools but highlight the application of IoT in mental health monitoring. Meanwhile, other studies conduct systematic reviews encompassing 41 studies utilizing smart devices and wearable technology in mental health monitoring, yet without specifying the software used. Another research proposes an experimental design to test a wearable sensor-based machine learning stress monitoring system. On the other hand, there are literature survey reports on the use of wearable sensors in mental health monitoring without providing details of the reviewed study methodologies. Other studies explore the literature using a scoping review method to gather information on mental health technology, identifying 37 relevant scientific articles. This review emphasizes the need for rigorous methodological approaches to effectively understand and apply technology in mental health monitoring and intervention. Overall, this literature review highlights the importance of developing technology that can enhance mental health monitoring and intervention. The application of IoT, wearable devices, and smart sensors can be a potential solution but requires a multidisciplinary approach and meticulous methodology to optimize their use in clinical practice
A Review on Growth Factors in Digital Start-ups: Digital Marketing, Scaling, Adaptation, Advanced Tech Fatmah, Siti; Samsidar; Atnang, Muhammad; Mustamin, Syaiful Bachri; Sahriani; Mar, Nur Azaliah; Fajar, Nurhikmah
Journal of Scientific Insights Vol. 1 No. 1 (2024): June
Publisher : Science Tech Group

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69930/jsi.v1i1.73

Abstract

Understanding MRBS (Massive and Rapid Business Scaling) is critical in the context of digital start-ups as it helps maximize the use of limited office space, better manage time, and support effective collaboration. This study aims to explore the concept of MRBS in the context of digital start-ups and identify the factors that drive the phenomenon. The focus of this study is on the significant increase in MRBS driven by recent advances in digitization, despite only about 3% of start-ups ever reaching a market valuation of $1 billion (USD) or more. Using an inductive qualitative research approach through 53 semi-structured interviews with start-up founders, executives, and advisors, this study seeks to fill the gap in previous literature that has not comprehensively explored the drivers of MRBS in the context of digital start-ups. The findings of this study reveal seven core drivers that contribute to the MRBS process, namely access to capital, product innovation, technology adoption, competent team, marketing strategy, networks and partnerships, and scale of operations. In addition, this study also identified several areas of tension that arise in the MRBS process, such as pressure for rapid growth, risk of failure, and challenges in maintaining corporate culture. Other related literature studies also explored the potential impact of extended digital marketing and its influence on the growth of startups. This research develops a macrodynamic framework that describes the drivers of startup growth supported by digital marketing and analyzes the differences in the use of B2B and B2C digital marketing, as well as the impact of new technologies on digital marketing. The results of these two studies are expected to provide researchers and practitioners with valuable insights into the MRBS phenomenon and the potential of digital marketing in supporting startup growth. Thus, this research contributes to understanding how start-ups can achieve large and rapid business scale in today's digital era.
A Systematic Review of Telemedicine in Pediatrics Evidence from Randomized Controlled Trials Atnang, Muhammad; Samsidar, Samsidar; Bachri Mustamin, Syaiful
Journal of Scientific Insights Vol. 1 No. 1 (2024): June
Publisher : Science Tech Group

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69930/jsi.v1i1.81

Abstract

Telemedicine has emerged as an important innovation in pediatric health care delivery, particularly in addressing issues of accessibility and quality of care. This systematic review focused on randomized controlled trials (RCTs) conducted over the last decade to assess the effectiveness of telemedicine in pediatric care. This review identified that telemedicine increases healthcare accessibility, increases patient satisfaction, and contributes to favorable treatment outcomes across a variety of health conditions. However, a significant gap identified was the lack of an up-to-date systematic review evaluating the current evidence regarding telemedicine in pediatrics. In response to this gap, this study provides a systematic and comprehensive evaluation, without using a specific theoretical framework, but rather focusing on the integration of recent evidence. Amid ongoing debate regarding the effectiveness of telemedicine in the pediatric setting, this review emphasizes the need for more RCTs to fill gaps in the existing literature. Overall, this literature review directs the future research agenda by highlighting the need for a more holistic and integrated approach to the utilization of telemedicine in pediatrics. With a focus on improving the quality of services and developing adequate policies, this research aims to make a significant contribution to the understanding and implementation of telemedicine in the future.
The Application of Machine Learning and Intelligent Sensors for Real-Time Air Quality Monitoring: A Literature Review Mustamin, Syaiful Bachri; Atnang, Muhammad; Sahriani, Sahriani; Fajar, Nurhikmah; Sari, Sri Kurnian; Pahlawan , Muammar Reza; Amrullah, Mujahidin
Journal of Scientific Insights Vol. 1 No. 3 (2024): October
Publisher : Science Tech Group

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69930/jsi.v1i3.183

Abstract

Air pollution is a global issue that has major consequences for human health and the environment. Accurate air quality prediction plays an important role in mitigating and preventing the negative impacts of air pollution. The thirteen sources analyzed in this literature study show a growing trend in the use of machine learning for air quality prediction, driven by the limitations of traditional methods and machine learning capabilities in efficiently processing complex data. This literature study examines a variety of commonly used machine learning models, such as Support Vector Regression (SVR), Random Forest, Gradient Boosting, and Long Short-Term Memory (LSTM), and evaluates their performance based on metrics such as RMSE, MAE, and R². The sources also highlight the importance of understanding the factors that affect air quality, including concentrations of various pollutants (PM2.5, PM10, NO2, CO, SO2, and ozone), meteorological data (temperature, humidity, wind speed, air pressure, precipitation, and temperature inversion), traffic data, and spatial-temporal variations. The integration of the Internet of Things (IoT) and machine learning is the main focus in the development of real-time air quality monitoring systems. IoT sensors enable the collection of real-time air quality and meteorological data, which are then processed using machine learning models to generate predictions. This literature study identifies several challenges in air quality prediction, such as data limitations, the complexity of air pollution dynamics, and ethical & privacy considerations. However, machine learning offers great potential to improve the accuracy of air quality predictions and monitoring, thus contributing to a healthier and more sustainable environment.
Transparansi dan Auditabilitas Data Pribadi dalam Layanan Berbasis Cloud Pada Proyek PACE: Studi Literatur Mustamin, Syaiful Bachri; Atnang, Muhammad; Sahriani; Hikmah , Nur; Samsidar
Jurnal Teknologi dan Sains Modern Vol. 1 No. 1 (2024): Mei-Juni
Publisher : CV. Science Tech Group

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69930/jtsm.v1i1.57

Abstract

Ulasan komprehensif ini mengeksplorasi pendekatan-pendekatan beragam dalam meningkatkan privasi dan keamanan dalam manajemen data di berbagai bidang. Studi pertama menyajikan kerangka konseptual yang bertujuan untuk memperkuat privasi dan keamanan dalam manajemen data kota cerdas dengan mengintegrasikan kecerdasan buatan dan pemodelan big data. Meskipun analisis data empiris absen, kerangka konseptual tersebut memberikan wawasan penting tentang kemungkinan kemajuan. Studi kedua menelusuri proyek Privacy-Aware Cloud Ecosystems (PACE), berfokus pada teknologi blockchain untuk meningkatkan transparansi dan auditabilitas dalam pemrosesan data pribadi berbasis awan. Meskipun secara utama berorientasi pada masa depan, teknologi yang dikembangkan menjanjikan peningkatan privasi dan keamanan data dalam komputasi awan. Tinjauan literatur dalam studi ketiga mengevaluasi tren dan tantangan dalam menerapkan keamanan dan blockchain dalam Internet of Multimedia Things (IoMT), memberikan wawasan berharga meskipun tanpa temuan empiris langsung. Terakhir, sebuah studi eksperimental memperkenalkan sistem pengolahan data berbasis blockchain dan differential privacy untuk komputasi perkotaan, melaporkan kinerja sistem dan peningkatan keamanannya. Meskipun metodologi beragam, setiap studi memberikan kontribusi pada diskusi yang lebih luas tentang privasi dan keamanan data, menawarkan wawasan, kerangka kerja, dan inovasi teknologi untuk penelitian dan implementasi praktis di masa depan.
Inovasi Pembelajaran Mesin untuk Deteksi Malware: Analisis Komprehensif dan Tinjauan Literatur Samsidar; Mustamin, Syaiful Bachri; Atnang, Muhammad; Sahriani; Fajar, Nurhikmah
Jurnal Teknologi dan Sains Modern Vol. 1 No. 1 (2024): Mei-Juni
Publisher : CV. Science Tech Group

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69930/jtsm.v1i1.58

Abstract

Penelitian tentang deteksi malware menggunakan pembelajaran mesin dan teknik deep learning telah menjadi topik yang menarik dalam beberapa tahun terakhir. Kombinasi fitur statis dan dinamik telah terbukti efektif dalam meningkatkan akurasi deteksi hingga 95%, sementara pendekatan ensemble learning juga menunjukkan peningkatan yang signifikan, mencapai akurasi hingga 97% untuk malware nol-hari. Implikasi temuan ini sangat penting dalam konteks keamanan siber, dengan kemampuan deteksi yang lebih baik dapat membantu melindungi sistem dan infrastruktur kritis dari serangan malware yang semakin canggih. Namun, ada beberapa batasan dalam penelitian ini, termasuk fokus yang terbatas pada tinjauan literatur dan tidak mencakup evaluasi eksperimental langsung. Oleh karena itu, penelitian lanjutan diperlukan untuk menguji metode ini pada dataset yang lebih beragam dan lingkungan operasional yang lebih realistis. Kontribusi dari penelitian ini terletak pada pengembangan solusi deteksi malware yang lebih efektif dan akurat menggunakan pendekatan pembelajaran mesin dan deep learning. Pertanyaan dan arah penelitian baru termasuk investigasi efektivitas metode dalam lingkungan produksi, pengembangan model hibrid yang menggabungkan pembelajaran mesin dengan teknik lain, serta eksplorasi penggunaan pembelajaran mesin untuk deteksi malware pada perangkat Internet of Things (IoT). Penelitian ini juga menyoroti pentingnya mempertimbangkan variabel tambahan seperti kompleksitas malware, metode penyebaran, dan dampak terhadap sistem target dalam penelitian mendatang. Secara keseluruhan, temuan ini mendukung gagasan bahwa pembelajaran mesin dan deep learning memiliki potensi besar dalam mengatasi tantangan deteksi malware yang semakin kompleks dan dinamis, dengan implikasi yang luas dalam meningkatkan keamanan siber.
Studi Literatur Deep Learning dan Machine Learning untuk Analisis dan Prediksi Pasar Saham: Metodologi, Representasi Data, dan Studi Kasus Sari, Eka Purnama; Mustamin, Syaiful Bachri; Atnang, Muhammad; Sahriani; Fajar, Nurhikmah
Jurnal Teknologi dan Sains Modern Vol. 1 No. 1 (2024): Mei-Juni
Publisher : CV. Science Tech Group

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69930/jtsm.v1i1.59

Abstract

Penelitian ini mengkaji penggunaan model Machine Learning (ML) dan Deep Learning (DL) untuk peramalan harga saham, sebuah topik yang semakin relevan di sektor keuangan. Model ensemble "Random Forest + XG-Boost + LSTM" terbukti memiliki kinerja yang lebih baik dibandingkan model ML dan DL lainnya, menunjukkan bahwa integrasi model dapat meningkatkan akurasi prediksi. Penelitian lain juga menyoroti potensi jaringan deep learning untuk analisis pasar saham, menemukan bahwa jaringan saraf dapat mengekstrak informasi tambahan yang meningkatkan kinerja prediksi, meskipun sangat bergantung pada representasi data yang digunakan. Penggabungan variabel sentimen publik dari media sosial dengan variabel teknis dapat meningkatkan akurasi prediksi, terutama dalam kondisi pasar yang tidak stabil. Berdasarkan tinjauan pustaka komprehensif terhadap lebih dari 150 artikel dan menemukan bahwa algoritma ML, terutama RNN, menunjukkan kinerja unggul dalam prediksi pasar keuangan. Model yang menggunakan sentimen dari media sosial untuk memprediksi pergerakan harga saham, menunjukkan bahwa informasi sentimen dapat memberikan informasi tambahan yang signifikan untuk prediksi. Penelitian-penelitian ini menegaskan pentingnya penggunaan model ML dan DL dalam peramalan harga saham serta manfaat integrasi variabel non-teknis seperti sentimen dari media sosial dengan variabel teknis. Meski demikian, diperlukan penelitian lebih lanjut untuk memperluas cakupan data dan menguji generalisasi model pada berbagai pasar saham global serta periode waktu yang lebih panjang.
Co-Authors Amrullah, Mujahidin Andika Mayansara Saboli Anggriani, Dwi Bachri Mustamin, Syaiful Banudi, La Dwiyanti Dwiyanti, Dwiyanti Eka Purnama Sari, Eka Purnama Fajar, Nurhikmah Ferdiansyah Hikmah , Nur Kiki Mustamin, Syaiful Bachri Nur Azaliah MAR, Nur Azaliah Nurul Hidayatullah, Nurul Pahlawan , Muammar Reza Ramli, Agus Salim riskyawan Sahriani Sahriani, Sahriani Samsidar Samsidar Samsidar Sari, Sri Kurnian Siti Fatmah, Siti Surahmawanti, Mita