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All Journal Jurnal Teknologi Sistem Informasi dan Sistem Komputer TGD Media of Computer Science
Asriani, Ika
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Languange, Linguistic, Communication & Media Library & Information Science

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Expert System for Determining Diseases and Pests in Seaweed Using Forward Chaining (Case Study : Watorumbe Village, Mawasangka Tengah) Asriani, Ika; Muchtar, Mutmainnah; Ismail, Rima Ruktiari; Paliling, Alders; Sya'ban, Kharis; Karim, Rahmat
Media of Computer Science Vol. 1 No. 1 (2024): June 2024
Publisher : CV. Digital Innovation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69616/mcs.v1i1.175

Abstract

Seaweed is a marine organism that plays a crucial role in both ecosystem and economy. However, it often faces attacks from diseases and pests that can jeopardize the productivity and sustainability of the seaweed industry. Hence, the development of an expert system to diagnose seaweed diseases and pests becomes imperative. This research aims to develop an Expert System for Determining Diseases and Pests in Seaweed using the Forward Chaining method, with a case study conducted in the Watorumbe Village, Mawasangka Tengah Sub-district, Southeast Sulawesi. The Forward Chaining method is employed to identify symptoms appearing in seaweed and determine potential diseases or pests. Testing is carried out with 30 data samples compared against expert diagnoses, resulting in an accuracy rate of 90%. Therefore, this system has the potential to assist seaweed farmers in diagnosing diseases and pests more quickly and accurately, thereby enhancing the productivity and sustainability of seaweed cultivation efforts.
Pengelompokan Data Pertumbuhan dan Kontribusi Ekonomi Indonesia Menurut Provinsi Menggunakan Metode K-Means Clustering Maulidiah, Rizka; Muchtar, Mutmainnah; Fitri, Nurul Aisyah; Asriani, Ika; Yasmine, Mutiara Putri
Jurnal Teknologi Sistem Informasi dan Sistem Komputer TGD Vol. 6 No. 2 (2023): J-SISKO TECH EDISI JULI
Publisher : STMIK Triguna Dharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53513/jsk.v6i2.7769

Abstract

Since the Covid-19 pandemic in recent years, several countries have been preoccupied with how to break the chain of the spread of the virus, including Indonesia, whose government policies are considered contradictory in efforts to improve the economy in Indonesia so that the economy has decreased in many regions in Indonesia. The importance of restoring the regional economy in order to improve people's welfare requires the government to be able to pay more attention to the region. to group the economic growth and economic contribution of Indonesian cities using data mining techniques with the K-Means Clustering algorithm using rapid miner tools. This research will classify into 3 clusters, high, medium, and low clusters. The results obtained for Indonesia's economic growth resulted in 2 provinces for high clusters, 1 low cluster, and 31 provinces for medium clusters. For the economic contribution of high clusters are 3 provinces, low clusters are 24 provinces, and medium clusters are 7 provinces. This method is considered to work well on the object of this research data.
Co-Authors alders paliling Fitri, Nurul Aisyah Ismail, Rima Ruktiari Maulidiah, Rizka Mutmainnah Muchtar Rahmat Karim Syaban, Kharis Yasmine, Mutiara Putri