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All Journal IJISTECH (International Journal Of Information System & Technology) IJISTECH
Ulfah Indriani
Universitas Potensi Utama, Medan, Indonesia
Computer Science & IT Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Social Sciences

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ARIMA Method Implementation for Prediction of Oyster Mushroom Production Nita Syahputri; Ommi Alfina; Labuan Nababan; Ulfah Indriani
IJISTECH (International Journal of Information System and Technology) Vol 6, No 4 (2022): Decembar
Publisher : Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/ijistech.v6i4.270

Abstract

Ad-Dhuha Farm is an MSME (Micro, Small and Medium Enterprises) business engaged in the production of oyster mushrooms. In producing oyster mushrooms for sale, Ad-Dhuha usually estimates based on production logbooks. So that sometimes the results of predictions that are made miss estimates, this is because there is no proper calculation based on the data used so that there is a buildup of oyster mushroom supplies. This affects the progress of MSME businesses (Micro Small and Medium Enterprises) because seeing from the need to pay workers, invoices for operational needs and so on so that MSME businesses (Micro Small and Medium Enterprises) suffer losses. This study uses the ARIMA method to predict the amount of oyster mushroom production in Ad-Dhuha. With the ARIMA method, Ad-Dhuha can predict the amount of production so that there is no accumulation of oyster mushrooms.
ARIMA Method Implementation for Prediction of Oyster Mushroom Production Nita Syahputri; Ommi Alfina; Labuan Nababan; Ulfah Indriani
IJISTECH (International Journal of Information System and Technology) Vol 6, No 4 (2022): Decembar
Publisher : Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/ijistech.v6i4.270

Abstract

Ad-Dhuha Farm is an MSME (Micro, Small and Medium Enterprises) business engaged in the production of oyster mushrooms. In producing oyster mushrooms for sale, Ad-Dhuha usually estimates based on production logbooks. So that sometimes the results of predictions that are made miss estimates, this is because there is no proper calculation based on the data used so that there is a buildup of oyster mushroom supplies. This affects the progress of MSME businesses (Micro Small and Medium Enterprises) because seeing from the need to pay workers, invoices for operational needs and so on so that MSME businesses (Micro Small and Medium Enterprises) suffer losses. This study uses the ARIMA method to predict the amount of oyster mushroom production in Ad-Dhuha. With the ARIMA method, Ad-Dhuha can predict the amount of production so that there is no accumulation of oyster mushrooms.
Mobile-Based Application Implementing the Dempster-Shafer Method for Diagnosing Post-Traumatic Stress Disorder (PTSD) Dini Ridha Dwiki Putri; Muhammad Reza Fahlevi; Fetty Ade Putri; Ulfah Indriani; Miftahul Mardiyah; Diaz Andreansyah
IJISTECH (International Journal of Information System and Technology) Vol 9, No 1 (2025): The June Edition
Publisher : Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/ijistech.v9i1.403

Abstract

Post-Traumatic Stress Disorder (PTSD) is a serious mental health condition that can develop after an individual experiences a traumatic event. Early diagnosis of PTSD symptoms is crucial to prevent more severe psychological and social impacts. This study aims to develop a mobile-based expert system application that implements the Dempster-Shafer method to independently diagnose the confidence level of PTSD symptoms. The system is designed with three main components: biodata input, a symptom selection interface based on 9 indicator questions, and an inference engine utilizing the Dempster-Shafer method with a Likert scale to assess the severity of trauma experienced by respondents. Testing was conducted on 47 respondents, and the analysis results showed that 19.1% were in the safe category, 4.3% in mild PTSD, 12.8% in moderate PTSD, and 63.8% in severe PTSD. Validity testing indicated that all symptom items had correlation values (r-calculated) greater than 0.2876, confirming their validity, and the instrument’s reliability was measured at α = 0.806. The application is capable of providing an initial diagnosis along with recommended actions based on the severity level of PTSD. This study demonstrates that applying the Dempster-Shafer method in a mobile application offers an effective, efficient, and accessible alternative solution for early PTSD diagnosis, especially for individuals with limited access to professional mental health services.
Mobile-Based Application Implementing the Dempster-Shafer Method for Diagnosing Post-Traumatic Stress Disorder (PTSD) Dini Ridha Dwiki Putri; Muhammad Reza Fahlevi; Fetty Ade Putri; Ulfah Indriani; Miftahul Mardiyah; Diaz Andreansyah
IJISTECH (International Journal of Information System and Technology) Vol 9, No 1 (2025): The June Edition
Publisher : Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/ijistech.v9i1.403

Abstract

Post-Traumatic Stress Disorder (PTSD) is a serious mental health condition that can develop after an individual experiences a traumatic event. Early diagnosis of PTSD symptoms is crucial to prevent more severe psychological and social impacts. This study aims to develop a mobile-based expert system application that implements the Dempster-Shafer method to independently diagnose the confidence level of PTSD symptoms. The system is designed with three main components: biodata input, a symptom selection interface based on 9 indicator questions, and an inference engine utilizing the Dempster-Shafer method with a Likert scale to assess the severity of trauma experienced by respondents. Testing was conducted on 47 respondents, and the analysis results showed that 19.1% were in the safe category, 4.3% in mild PTSD, 12.8% in moderate PTSD, and 63.8% in severe PTSD. Validity testing indicated that all symptom items had correlation values (r-calculated) greater than 0.2876, confirming their validity, and the instrument’s reliability was measured at α = 0.806. The application is capable of providing an initial diagnosis along with recommended actions based on the severity level of PTSD. This study demonstrates that applying the Dempster-Shafer method in a mobile application offers an effective, efficient, and accessible alternative solution for early PTSD diagnosis, especially for individuals with limited access to professional mental health services.
Co-Authors Alfina, Ommi Diaz Andreansyah Dini Ridha Dwiki Putri Fetty Ade Putri Miftahul Mardiyah Muhammad Reza Fahlevi Nababan, Labuan Nita Syahputri