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All Journal Jurnal Media Infotama Proceeding SENDI_U Jurnal Teknologi Komputer dan Sistem Informasi Journal of Innovation and Future Technology (IFTECH) Journal of Applied Engineering and Technological Science (JAETS) JURNAL PENELITIAN PERAWAT PROFESIONAL Indonesian Journal of Electrical Engineering and Computer Science Abdimasku : Jurnal Pengabdian Masyarakat Jurnal PkM: Pemberdayaan Masyarakat International Journal of Computer and Information System (IJCIS) Jurnal Pengabdian kepada Masyarakat JAST Jurnal Sains Teknologi dan Sistem Informasi Jurnal Dinamika Informatika (JDI) Jurnal Ilmu Komputer dan Teknologi (IKOMTI) Empowerment Journal Indonesian Journal of Information Technology and Computing (IMAGING) Inovasi Jurnal Pengabdian Masyarakat (IJPM) Jurnal Riset Sistem dan Teknologi Informasi PEDAMAS (Pengabdian Kepada Masyarakat) Jurnal TAM (Technology Acceptance Model) JTKSI (Jurnal Teknologi Komputer dan Sistem Informasi)
Ismail Setiawan
Universitas 'Aisyiyah Surakarta
Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mechanical Engineering Medicine & Pharmacology Nursing Public Health Social Sciences Veterinary Other

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Journal : Journal of Innovation and Future Technology (IFTECH)

 1 
MENGATASI KEMACETAN DI LAMPU MERAH DENGAN PENDEKATAN IMAGE PROCESSING Ismail Setiawan
Journal of Innovation And Future Technology (IFTECH) Vol 4 No 2 (2022): Vol 4 No 2 (August 2022): Journal of Innovation and Future Technology (IFTECH)
Publisher : LPPM Unbaja

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47080/iftech.v4i2.2117

Abstract

The phenomenon of congestion in a big city is generally caused by the large number of vehicles while roads do not develop every year. The growth in the number of vehicles on the highway contributes to a large number of causes of congestion. Congestion often occurs at red light intersections. Various solutions have been implemented such as limiting the number of vehicles based on odd-even numbers by date, limiting motorized vehicles such as motorbikes that are not allowed to pass through the main road, increasing the number of public vehicles and arranging the departure schedule for school children and office employees. The solution has been carried out but there are still long queues of vehicles. One of the reasons for this is because the number of vehicle queues is long while the timer that shows the time to walk or the green light is short. Intelligent systems technology-based approach can be used to overcome these problems. Because humans have limitations to do so, the task can be done by computers. The result of this activity is that the right camera position is next to the traffic timer and the best time to read the image is during the day
DATA SCIENCE: PENDEKATAN DAN LANGKAH PRAKTIS DENGAN EXCEL Ismail Setiawan; Aisyah Mutia Dawis
Journal of Innovation And Future Technology (IFTECH) Vol 5 No 1 (2023): Vol 5 No 1 (February 2023): Journal of Innovation and Future Technology (IFTECH)
Publisher : LPPM Unbaja

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47080/iftech.v5i1.2457

Abstract

The steps in conducting data science activities consist of several stages, namely problem identification, understanding the current business, data collection, data processing, and making decisions based on insights. Researchers who engage in data science activities are often referred to as data scientists. In their process, data scientists use applications to facilitate their data science activities. One application that can be used by data scientists is Excel. Excel has features that can handle a certain amount of data. However, for the initial steps towards becoming a data scientist, Excel is a good application with features that make it easier for researchers to conduct data science activities. Data that can be managed by Excel is not more than 1 million rows, as Excel only has a maximum of 1,048,576 rows and 16,384 columns. Nevertheless, the features in Excel are already powerful, such as error detection, removing duplicate data, correcting error values, detecting outliers, handling missing data, and validating data. This study discusses the functions of these features in an effort to promote data science for beginner data scientists.
EXPLORING COMPLEX DECISION TREES: UNVEILING DATA PATTERNS AND OPTIMAL PREDICTIVE POWER Ismail Setiawan; Renata Fina Antika Cahyani; Irfan Sadida
Journal of Innovation And Future Technology (IFTECH) Vol 5 No 2 (2023): Vol 5 No 2 (August 2023): Journal of Innovation and Future Technology (IFTECH)
Publisher : LPPM Unbaja

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47080/iftech.v5i2.2829

Abstract

This research investigates the development and analysis of decision tree models in the context of classification tasks. Decision tree models were developed without employing pruning or pre-pruning techniques and were tested on relevant datasets. The research findings demonstrate that complex models without pruning achieved the highest level of accuracy in classifying data. This study was inspired by the potential issue of students facing the risk of not completing their studies (dropout), which could lead to a decline in the college's accreditation rating. Therefore, this model was devised to assist in identifying factors that could influence this outcome as a preventative measure. Additionally, we successfully generated clear visualizations of the decision trees, enhancing the understanding of the model's decision-making process. This research provides insights into the adaptability of decision tree models within this specific case and showcases their potential for enhancing decision-making across various contexts. These findings encourage further discussions on the benefits of pruning methods within this specific context and the broader application potential of decision tree models.
Co-Authors Achmad Noerkhaerin Putra Aisyah Mutia Dawis Ardhani, Rahmad Dedi Gunawan Dewanta, Wika Eko Purbiyanto Elfrida Ratnawati Erna Kumalasari Nurnawati Erna Kumalasari Nurnawati Fatah Yasin, Ilham Fuad Dwi Artha Hanif Mustikasari Hanung Adi Nugroho Heru Supriyono Irfan Sadida Ita Permatahati Kresno Ario Tri Wibowo Lidiana, Exda Hanung Mieska Despitasari Muhammad Fadhil, Taqy Nafisah Miftahul Jannah, Ilma Nurhayati Nurhayati Pantjarani, Ari Renata Fina Antika Cahyani Rifa'i Wahid Ahmad Iktisom Rifani, Badriyah Sigit Setiyanto Supriono, Heru Surya Kusuma, Ridho Tri Desianti, Yuninda Ulun, Nutfah Ailul Wika Dewanta Yuniarti, Arinia Hasna