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All Journal Jurnal Informatika Kaputama (JIK) SENSITEK Jurnal Pengabdian kepada Masyarakat Information System and Data Science Majalah Ilmiah METHODA TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi Jurnal Inovasi Informatika dan Bisnis Digital (JIIBD) Jurnal Pemberdayaan Kepada Masyarakat
Fithry Tahel
Universitas Potensi Utama
Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Environmental Science Languange, Linguistic, Communication & Media Medicine & Pharmacology Public Health Social Sciences Other

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Sistem Pakar Diagnosa Penyakit Obsessive Compulsive Disorder Hoarding Menggunakan Metode Certainty Factor Berbasis PHP Rahmaini Hifzah Ruslan; Fithry Tahel
TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi Vol 5 No 2 (2025): TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/tamika.Vol5No2.pp144-148

Abstract

Obsessive-Compulsive Disorder, or OCD, is a mental health disorder characterized by obsessive thoughts and compulsive actions that are difficult to control. Providing appropriate treatment is crucial to prevent further impact on the quality of life of OCD sufferers. Unfortunately, initial diagnosis is often hampered by time constraints and difficult access to doctors or specialists. Therefore, researchers recommend a PHP-based expert system using the Certainty Factor method. This system mimics the way doctors think when diagnosing OCD, based on symptoms selected by the user. The Certainty Factor is used to calculate the certainty of the diagnosis based on the entered symptoms. The analysis shows that this system is capable of diagnosing with high accuracy, even reaching 100% in some OCD cases. These results demonstrate that expert systems can be a powerful tool for early detection of OCD, allowing for faster and more targeted treatment and care.
Penerapan Metode One-Time Pad Chiper untuk Mengamankan Data Karyawan Pada PT. Transima Citra Indo Consultant Salsa Carissa Sinaga; Fithry Tahel
TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi Vol 5 No 2 (2025): TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/tamika.Vol5No2.pp181-186

Abstract

PT. Transima Citra Indo Consultant, a company engaged in the field of tax consulting, requires a reliable data security system to maintain the confidentiality of sensitive information. One of the cryptographic methods that can be used is the One-Time Pad (OTP) method. One-Time Pad is an encryption algorithm with a very high level of security because it uses a unique key throughout the original message, thus ensuring that the encrypted message cannot be cracked as long as the key remains confidential. Therefore, this study will discuss the application of One-Time Pad in protecting employee data at PT. Transima Citra Indo Consultant as an effort to improve company data security. The results of this study are expected to provide significant benefits to PT. Transima Citra Indo Consultant in securing employee data, and also ensuring that only authorized parties can access it.
Penerapan Big Data Analyst terhadap Pengiriman Barang Cacat Menggunakan Metode K-Means Ardhana Febriansyah; Fithry Tahel
TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi Vol 5 No 2 (2025): TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/tamika.Vol5No2.pp244-249

Abstract

PT ID Express, a delivery service company, faces the challenge of high levels of damage to goods during the shipping process. This damage ranges from minor scratches or dents to severe damage such as breakage or destruction, which directly impacts customer satisfaction and the company's reputation. To address this issue, a data-driven approach capable of comprehensively identifying patterns and causal factors for damage is required. This study aims to analyze the types of damage to goods based on shipping data using the K-Means Clustering method. This method is used to group damaged goods data into several clusters based on the level of similarity in their characteristics. The results show that the damage data can be grouped into two main clusters: minor damage and major damage, each consisting of five dominant types of damage. Through this clustering, companies can gain a better understanding of frequently occurring damage patterns and can design more effective preventive measures. This research is expected to serve as a reference in the application of big data analysis in the logistics sector to improve service quality and reduce the risk of damage to goods during the distribution process.
Co-Authors Alfa Saleh Ardhana Febriansyah Efani Desi Erwin Ginting Eunike Aprilia Sihombing Farhan Ramadhan Jekni M Siregar Mas Ayoe Elhias Nuraini Sri Bina Prayono Agil Suherman Rahmaini Hifzah Ruslan Ratih Puspitasari Risa Anggraini Roully Sientia Salsa Carissa Sinaga Siti Aliyah