cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Mesran
Contact Email
mesran.skom.mkom@gmail.com
Phone
+6282161108110
Journal Mail Official
tin.journal@fkpt.org
Editorial Address
Jalan Sisingamangaraja No. 338, Medan, Sumatera Utara
Location
Kota medan,
Sumatera utara
INDONESIA
TIN: TERAPAN INFORMATIKA NUSANTARA
Published by Forum Kerjasama Pendidikan TInggi
ISSN : -     EISSN : 27227987     DOI : -
Core Subject : Economy, Health, Science, Education, Art, Social, Engineering,
Aerospace Engineering Arts Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Dentistry Earth & Planetary Sciences Economics, Econometrics & Finance Energy Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Neuroscience Nursing Physics Social Sciences Veterinary Other
Jurnal TIN: TERAPAN INFORMATIKA NUSANTARA memuat tentang Kajian Bunga Rampai dari berbagai ide dan hasil penelitian para peneliti, mahasiswa, dan dosen yang berkompeten di bidangnya dari berbagai disiplin ilmu seperti: Komputer, Informatika, Industri, Elektro, Telekomunikasi, Kesehatan, Agama, Pertanian, Pembelajaran, Pendidikan, Teknologi Pendidikan, Ekonomi dan Bisnis, Manajemen, Akuntansi, dan Hukum
Arjuna Subject : Umum - Umum
Articles 6 Documents
 1 
Search results for , issue "Vol 4 No 10 (2024): March 2024" : 6 Documents clear
Use of Work Sampling to Determine Standard Time in Sales Outlet Performance: A Case Study Rosyidi, Moh Ririn; Izzah, Nailul
TIN: Terapan Informatika Nusantara Vol 4 No 10 (2024): March 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v4i10.4336

Abstract

Working time measurement is an attempt to identify the typical time required to finish a job in order to achieve a balance between output produced and labour performed. During the investigation, the problem was that employee working hours varied, thus it was required to enhance the working hours of sales outlet personnel. The findings of observations in non-productive areas were 0.27, indicating that there was still allowance time occurring at that site, indicating that research must be conducted continually. Trying to achieve standard time throughout sales outlet working hours and looking for strategies to boost outlet worker efficiency during the job process. The work sample approach was employed in the company's research, and the results showed that the standard time for a sales outlet to serve each customer required a minimum of p.1= 6.162 minutes, p.2= 8.453 minutes, for a total of 18.17 minutes. The standard productivity that two outlet employees must achieve during one shift (6 working hours) can be calculated using the standard time computation, which is a minimum of 19.81 ≈ 20 pcs of products sold.
Analisis Sentimen Jogja Darurat Sampah di Twitter menggunakan Ekstraksi Fitur Model Word2Vec dan Convolutional Neural Network Yusanto, Yoga; Akbar, Mutaqin
TIN: Terapan Informatika Nusantara Vol 4 No 10 (2024): March 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v4i10.4952

Abstract

Due to a waste emergency, the Special Region of Yogyakarta has garnered public attention and sparked discussions. Numerous community groups express their opinions through various social media platforms, especially Twitter. It's undeniable that Twitter is currently one of the places for freely expressing opinions. Therefore, sentiment analysis plays a crucial role in efforts to categorize public opinions on something trending or viral into three categories: positive, negative, and neutral. In this study, the dataset was obtained using scraping techniques and the tweetscraper tool from the APIFY actor web.harvester/easy-twitter-search-scraper. The method employed in this analysis is the Convolutional Neural Network (CNN) classification method using Word2Vec model extraction. The study involves 505 tweets in Bahasa Indonesia with the hashtags #JogjaDaruratSampah (#JogjaDaruratSampah) and #TPSTPiyungan as data. Out of these, 381 tweets are utilized as training data, and the remaining 124 tweets are used as test data. The highest accuracy in testing the training data was achieved in the 19th epoch with a 90% accuracy rate. It can be concluded from the testing process that this study can identify positive, negative, and neutral sentiments with an accuracy of 53%. The sentiment analysis results indicate a significant amount of negative tweets, accounting for 49.7% of the total 505 tweets.
AR-FootIN 4.0 : Aplikasi Pengenalan Teknologi Industri 4.0 Pada Bidang Alas Kaki Berbasis Mobile Augmented Reality Prananda, Alifia Revan; Marwanto, Marwanto; Frannita, Eka Legya; Hidayat, Anwar
TIN: Terapan Informatika Nusantara Vol 4 No 10 (2024): March 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v4i10.4956

Abstract

Rapid development of technology gave a positive impact on the footwear industry. The emergence of various types of technology as part of the industrial revolution 4.0 has greatly helped various types of work in industry. However, technology also need to be supported by good quality resource. Knowledge regarding how to use and maintain these technologies is needed so that the benefits of these technologies can be utilized. An alternative way is by developing good quality of human resource to being proficient in using technology. Furthermore, cultivating technological literacy is also one of the essential factors. Regarding to this situation, we proposed research that aims to develop the AR-FootIN 4.0 application as a learning media for introducing industry 4.0 in the footwear sector. This learning media is developed by employing mobile augmented reality. The proposed learning media is developed by using the SDLC method. The resulted learning media is then evaluated by conducting two types of evaluation, which are expert evaluation and user evaluation. The results of expert evaluation and user evaluation obtain a percentage of 93.33% and 86% respectively, which means that the feasibility of the application to support the technological literacy process in the footwear industry is very good.
Klasifikasi Dialek Bahasa Inggris British dan Amerika menggunakan Support Vector Machine Kuswandaru, Kuswandaru; Akbar, Mutaqin
TIN: Terapan Informatika Nusantara Vol 4 No 10 (2024): March 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v4i10.4965

Abstract

English has become an international language used in various fields, including education, business, and tourism. Indonesia, having become a member of the AEC (Asean Economic Community), makes it increasingly important for Indonesian society, especially the younger generation, to master English proficiently and accurately. English, as an international language, encompasses numerous dialects, such as British and American dialects. This research is motivated by the issue that differences between British and American English dialects can affect understanding and communication in educational, business, and everyday life contexts. Identifying and classifying dialects in English speech is crucial to aid both native and non-native speakers in better understanding communication contexts. This study aims to develop a classification method using the Support Vector Machine (SVM) algorithm to distinguish between British and American English dialects in speech. By leveraging SVM, this research will attempt to identify linguistic features that differentiate between these dialects, such as intonation, vowels, consonants, and rhythm patterns obtained from sound feature extraction using Mel Frequency Cepstral Coefficients (MFCC). In this model training phase, a dataset comprising 720 speech samples collected from various text-to-speech service provider websites is utilized to represent both dialects. Subsequently, the trained model is tested using 24 test data collected from original recordings of several individuals to evaluate its accuracy. The results of this research yield an accuracy rate of 91.6% on the model with a configuration of Cost value 1, gamma 0.001, and polynomial kernel. From these results, it can be concluded that this model exhibits a sufficiently high accuracy, with 2 misclassifications out of 24 test data.
Sistem Pendukung Keputusan Rekomendasi Program Ekskul Disekolah Menegah Kejuruan dengan Metode AHP dan TOPSIS Tanjung, Erti Belastari; Zufria, Ilka; Armansyah, Armansyah
TIN: Terapan Informatika Nusantara Vol 4 No 10 (2024): March 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v4i10.5011

Abstract

Extracurricular activities are vital for developing students' personalities, talents, and abilities beyond the standard curriculum. However, a lack of effective information dissemination about extracurricular activities poses a challenge. To address this issue, a decision support system using Analytic Hierarchy Process (AHP) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) methods is needed, along with increasing students' awareness of the importance of extracurricular activities. This research aims to recommend extracurricular programs to students in vocational high schools. Specific issues include developing a literature review, designing a user-friendly system, tools for web-based decision support system development, and implementing AHP and TOPSIS methods for extracurricular recommendation systems. The research will be conducted at SMK Muhammadiyah 9 Medan, focusing on 93 selected Class X students. Recommendation criteria will include intelligence level, interests, concentration, memory, commitment, willingness, creativity, health history, and parental consent, based on the results of students' psychological tests. The AHP method will be used for criteria weighting, and TOPSIS will rank recommended extracurricular programs based on priority values. The research aims to provide 9 recommended extracurricular programs tailored to students' interests and talents, enhance researchers' skills in implementing AHP and TOPSIS methods, improve extracurricular program management efficiency, and increase student satisfaction with extracurricular activities. The decision support system developed in this research will benefit researchers, school administrators, and students, improving program management and student participation in extracurricular activities.The implementation results of extracurricular recommendations using the AHP and TOPSIS methods achieved an accuracy of 74%. Changes in accuracy may be influenced by the criteria used and inputted, as well as the minimum extracurricular activities inputted by the user.
Sistem Penjadwalan Bimbingan Konseling dengan Menerapkan Algoritma Shortest Job First Fatyana, Nadya; Irawan, Muhammad Dedi; Nasution, Adnan Buyung
TIN: Terapan Informatika Nusantara Vol 4 No 10 (2024): March 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v4i10.5024

Abstract

Counseling guidance is a place for students to consult in overcoming problems and developing the potential of students. In order for counseling guidance to run effectively, scheduling is needed. The preparation of counseling guidance schedules takes a long time so that it causes increasingly long queues. In this study, the method used in research is Research and Development and the system development method uses Rapid Application Development. A website-based scheduling system that uses the Shortest Job First algorithm was developed to shorten long queues. Average waiting time can be reduced by finding the shortest job first, which solves the smallest average waiting time for various operations. By implementing Shortest Job First it can produce a good counseling schedule with a smaller waiting time thus reducing the level of long queues. The accuracy rate of this system is 98.18% accurate.

Page 1 of 1 | Total Record : 6

 1 

Filter by Year

2024 2024


Reset
Filter By Issues
All Issue Vol 6 No 9 (2026): February 2026 Vol 6 No 8 (2026): January 2026 Vol 5 No 12 (2025): May 2025 Vol 5 No 11 (2025): April 2025 Vol 5 No 10 (2025): March 2025 Vol 6 No 7 (2025): December 2025 Vol 6 No 6 (2025): November 2025 Vol 6 No 5 (2025): October 2025 Vol 6 No 4 (2025): September 2025 Vol 6 No 3 (2025): August 2025 Vol 6 No 2 (2025): July 2025 Vol 6 No 1 (2025): June 2025 Vol 5 No 9 (2025): February 2025 Vol 5 No 8 (2025): January 2025 Vol 4 No 12 (2024): May 2024 Vol 4 No 11 (2024): April 2024 Vol 4 No 10 (2024): March 2024 Vol 5 No 7 (2024): December 2024 Vol 5 No 6 (2024): November 2024 Vol 5 No 5 (2024): October 2024 Vol 5 No 4 (2024): September 2024 Vol 5 No 3 (2024): August 2024 Vol 5 No 2 (2024): July 2024 Vol 5 No 1 (2024): June 2024 Vol 4 No 9 (2024): February 2024 Vol 4 No 8 (2024): January 2023 Vol 3 No 12 (2023): May 2023 Vol 3 No 11 (2023): April 2023 Vol 3 No 10 (2023): March 2023 Vol 4 No 7 (2023): December 2023 Vol 4 No 6 (2023): November 2023 Vol 4 No 5 (2023): October 2023 Vol 4 No 4 (2023): September 2023 Vol 4 No 3 (2023): August 2023 Vol 4 No 2 (2023): July 2023 Vol 4 No 1 (2023): June 2023 Vol 3 No 9 (2023): February 2023 Vol 3 No 8 (2023): January 2023 Vol 2 No 10 (2022): Maret 2022 Vol 3 No 7 (2022): December 2022 Vol 3 No 6 (2022): November 2022 Vol 3 No 5 (2022): October 2022 Vol 3 No 4 (2022): September 2022 Vol 3 No 3 (2022): August 2022 Vol 3 No 2 (2022): July 2022 Vol 3 No 1 (2022): June 2022 Vol 2 No 9 (2022): Februari 2022 Vol 2 No 8 (2022): Januari 2022 Vol 1 No 12 (2021): Mei 2021 Vol 1 No 11 (2021): April 2021 Vol 1 No 10 (2021): Maret 2021 Vol 2 No 7 (2021): Desember 2021 Vol 2 No 6 (2021): November 2021 Vol 2 No 5 (2021): Oktober 2021 Vol 2 No 4 (2021): September 2021 Vol 2 No 3 (2021): Agustus 2021 (in press) Vol 2 No 3 (2021): Agustus 2021 Vol 2 No 2 (2021): Juli 2021 Vol 2 No 1 (2021): Juni 2021 Vol 1 No 9 (2021): Februari 2021 Vol 1 No 8 (2020): Januari 2021 Vol 1 No 7 (2020): Desember 2020 Vol 1 No 6 (2020): November 2020 Vol 1 No 5 (2020): Oktober 2020 Vol 1 No 4 (2020): TIN: September 2020 Vol 1 No 3 (2020): TIN: Agustus 2020 Vol 1 No 2 (2020): TIN: Juli 2020 Vol 1 No 1 (2020): Juni 2020 More Issue