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All Journal Jurnal Teknologi dan Manajemen Informatika SMATIKA KLIK (Kumpulan jurnaL Ilmu Komputer) (e-Journal) Jurnal Ilmiah KOMPUTASI JURNAL MEDIA INFORMATIKA BUDIDARMA INTECOMS: Journal of Information Technology and Computer Science IJISTECH (International Journal Of Information System & Technology) JURIKOM (Jurnal Riset Komputer) JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Building of Informatics, Technology and Science Jurnal Mantik Jurnal Teknologi Informasi dan Multimedia Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Jurnal Teknik Informatika C.I.T. Medicom JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Journal of Computer System and Informatics (JoSYC) Jurnal Sistem Komputer dan Informatika (JSON) Jurnal Teknologi Informatika dan Komputer IJISTECH KLIK: Kajian Ilmiah Informatika dan Komputer JUSTIN (Jurnal Sistem dan Teknologi Informasi) Smatika Jurnal : STIKI Informatika Jurnal
Agus Iskandar
Universitas Nasional
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Social Sciences

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Journal : Journal of Computer System and Informatics (JoSYC)

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Penerapan Multi-Objective Optimization by Ratio Analysis (MOORA) Dalam Penyeleksian Kelayakan Nasabah Penerima Kredit Agus Iskandar
Journal of Computer System and Informatics (JoSYC) Vol 4 No 1 (2022): November 2022
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v4i1.2499

Abstract

Rural Banks (BPR) play an important role in helping to solve financial problems for the community in providing credit, the bank must conduct careful research and calculations on customers. PT.BPR this bank has a difficult problem determining the amount of credit value desired by a customer. This is because assessing whether or not a customer is eligible to receive a credit score is not easy because it involves many factors that must be considered and analyzed properly. The Decision Support System is applied in this study as a system for selecting the eligibility of credit recipients. In determining the appropriate selection of credit recipients, they must meet criteria such as income, age, occupation, guarantee and number of dependents. Therefore, a Decision Support System (DSS) is needed in solving existing problems by applying the MOORA (Multi-Objective Optimization on the basis of Ratio Analysis) method which can generate preference values ​​from the first ranked alternative. So that the selection of the eligibility of credit recipients lies in alternative N3 on behalf of Nurhayati with a value of 0.22197.
Penerapan Data Mining Dalam Penentuan Proritas Pemesanan Produk Berdasarkan dengan Data Penjualan Barang Menggunakan Algoritma Apriori Agung Triayudi; Agus Iskandar
Journal of Computer System and Informatics (JoSYC) Vol 4 No 1 (2022): November 2022
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v4i1.2523

Abstract

Products cannot be separated from the buying and selling process carried out by customers and sellers. Products that are a priority are, of course, products that are favored by consumers or customers. However, companies often make mistakes in the process of determining which products to order for sale. Errors in the process of determining product orders can result in losses for the company. Therefore, it is necessary to handle this problem properly and appropriately. Therefore, the settlement that should be done should be done by processing the data on the sale of these goods. Data mining is a method that can be used for data processing. But the data processing process carried out in data mining is not done independently. The a priori algorithm is one of the algorithms found in the data mining association technique. The a priori algorithm is widely used to carry out the analysis process on sales data to find sales patterns in the company. The results of the study were 3 items that became the priority of ordering, namely ordinary pants, shorts and t-shirts. With a combination of item sets, if you buy ordinary pants, you also buy shorts with a support value of 10% and a confidence value of 20%. Other item combinations that you get: If you buy regular pants, you also buy T-shirts with a support value of 30% and a confidence value of 60% and the last combination of set items. If you buy shorts, you also buy T-shirts with a support value of 30% and a confidence value of 60%.
Penerapan Metode TOPSIS Dalam Sistem Pendukung Keputusan Kelayakan Penerima Pinjaman Kredit Agus Iskandar
Journal of Computer System and Informatics (JoSYC) Vol 4 No 2 (2023): Februari 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v4i2.2879

Abstract

In determining the eligibility of loan recipients at Finance companies, it is carried out with the approval of the Head Office, then the stage of checking customer data is carried out with the approval of the office manager. Furthermore, in analyzing the decision-making eligibility of customers receiving credit loans, using a manual system that takes a long time, therefore, a decision support system is needed that functions to minimize excess or lack of data. In this study, it will be explained how the process of determining alternative credit recipient customers applies the TOPSIS method. The research results will produce the highest ranking value which will be determined as the highest alternative. The data used in this study are 10 alternative data samples using 5 criteria, so that the results of calculations that have been carried out using 8 alternatives and 5 criteria produce the best alternative, namely alternative A2 above with a value of 0.848 as a recommendation for a customer who will be entitled to receive a loan from finance company.
Sistem Pendukung Keputusan Untuk Menentukan Kelayakan Pemberian Bantuan Usaha Mikro dengan Menggunakan Metode SAW dan SMART Dandi Putra; Agung Triayudi; Agus Iskandar
Journal of Computer System and Informatics (JoSYC) Vol 4 No 2 (2023): Februari 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v4i2.3003

Abstract

The purpose of this business capital assistance program is to help people develop businesses that were previously affected by the Covid-19 pandemic which caused small businesses such as street vendors and home businesses to experience a decrease in income. This program aims to help small and medium enterprises (MSMEs). Potential beneficiaries are currently still selected through meetings held by village heads, which require a longer decision-making process.Therefore it is very important to create a new system that can be used to assist those who are eligible to receive targeted funds. In this study, a decision support system application was made to make it easier for village heads to determine candidates who are entitled to receive assistance according to the criteria. A decision support system is a solution that can be used to find out the results of the selection of MSME beneficiaries using the SAW and SMART methods. The SAW and SMART methods use 10 alternative data to provide an assessment of the final result by ranking from the highest to the lowest alternative value, showing that the two methods produce data that is accurate and suitable when applied as a ranking. The calculation results generated by the system that has been tested with the results of manual calculations, both show the same results.
Sistem Pendukung Keputusan dalam Rekomendasi Penentuan Prioritas Program Pembangunan Daerah menerapkan Simple Additive Weighting Agus Iskandar
Journal of Computer System and Informatics (JoSYC) Vol 4 No 3 (2023): May 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v4i3.3462

Abstract

The government and institutions in an area have developed a program known as the RKPD (Regional Development Work Plan), which is an elaboration of the RPJMD (Regional Medium Term Development Plan). However, the Medan City Regional Development Planning Agency faces several problems in the development stage in the regions, including consideration of priority scales and elements of justice as well as a lack of a decision support system to determine development priority scales. Therefore, the Agency needs an appropriate system to determine development priorities, and one of the systems that can help solve this problem is the Decision Support System (SPK). The method used is SAW which was chosen because it is expected to provide effective results in determining the priority of regional development programs at the Medan City Regional Development Planning Agency. The research resulted that the regional highway development program with a value of 0.77705 has the highest priority to be prioritized for regional development programs at the Medan City Regional Development Planning Agency, because it has the highest ranking when compared to other alternatives.
Analisa Sistem Pakar Menggunakan Algoritma Teorema Bayes Untuk Mendiagnosa Penyakit Fibrodysplasia Ossificans Progressiva (FOP) Muhammad Naufal Rifqi; Agus Iskandar
Journal of Computer System and Informatics (JoSYC) Vol 4 No 4 (2023): August 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v4i4.4041

Abstract

Fibrodysplasia Ossificans Progressiva (FOP) adalah penyakit langka yang disebabkan oleh kelainan genetik yang menyebabkan pembentukan tulang tidak normal di jaringan lunak tubuh. Mendiagnosis FOP menimbulkan tantangan karena langkanya kasus dan gejala awal yang ambigu. Kedua, karakteristik unik FOP menyulitkan membedakannya dari penyakit lain. Pemahaman terbatas tentang FOP dapat menyebabkan kesalahan diagnosis. Harapan muncul dengan kemajuan kecerdasan buatan, khususnya sistem pakar, yang membantu mendiagnosis FOP berdasarkan pengetahuan terprogram. Teorema Bayes, sebuah sistem kecerdasan buatan, digunakan untuk menghitung probabilitas diagnosis. Hasil diagnosa menunjukkan kemungkinan 74% pasien menderita FOP. Metode ini menggunakan data relevan untuk menghitung probabilitas secara akurat dan memberikan estimasi tingkat kepercayaan dalam mendiagnosis penyakit. Hasil ini panduan bagi dokter menyusun rencana perawatan yang sesuai dan efektif. Penerapan teknologi ini berpotensi meningkatkan manajemen pasien dan kualitas hidup mereka dengan FOP. Tetap diingat bahwa diagnosis medis memerlukan konfirmasi melalui pemeriksaan menyeluruh oleh para profesional kesehatan. Keberhasilan teknologi kecerdasan buatan dalam mendiagnosis FOP memberikan harapan bagi pengembangan perawatan lebih lanjut untuk kondisi langka ini.
Seleksi Penerimaan Customer Service Dalam Sistem Pendukung Keputusan Dengan Menerapkan Metode OCRA Rifqi Habibi Sachrrial; Agus Iskandar
Journal of Computer System and Informatics (JoSYC) Vol 4 No 4 (2023): August 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v4i4.4042

Abstract

Penelitian ini membahas tentang seleksi penerimaan Customer Service (CS) menggunakan Sistem Pendukung Keputusan (SPK) dengan menerapkan metode OCRA (Optimized Cost-Risk Analysis). Tujuan dari penelitian ini adalah untuk memperbaiki efisiensi dan efektivitas proses seleksi penerimaan CS, sehingga perusahaan dapat merekrut individu yang paling sesuai untuk peran tersebut. Metode OCRA digunakan sebagai pendekatan dalam penelitian ini karena dapat mengatasi beberapa tantangan dalam seleksi penerimaan CS, seperti mengevaluasi kualifikasi dan kemampuan kandidat, serta meminimalkan risiko pemilihan individu yang tidak tepat. Penelitian ini menggunakan data historis dari CS yang sudah terbukti berhasil dalam pekerjaan mereka sebagai basis pembuatan model OCRA. Langkah-langkah penelitian meliputi pengumpulan data kualifikasi dan kinerja CS yang saat ini berada di dalam perusahaan, kemudian data ini digunakan untuk melatih dan menguji model OCRA. Model OCRA akan menggabungkan beberapa faktor, termasuk keahlian komunikasi, pengetahuan produk, perilaku dalam menangani pelanggan, dan karakteristik personal lainnya yang relevan untuk pekerjaan CS. Hasil dari penelitian ini diharapkan dapat memberikan kontribusi penting dalam proses seleksi penerimaan CS dengan cara mengidentifikasi kandidat terbaik yang memiliki potensi untuk mencapai keberhasilan dalam peran tersebut. Selain itu, penggunaan metode OCRA juga diharapkan dapat mengurangi risiko perekrutan CS yang kurang sesuai, sehingga dapat mengurangi biaya dan waktu yang diperlukan dalam proses rekrutmen ulang. Setelah dilakukan tahapan-tahapan perhitungan dengan menggunakan metode OCRA, maka didapatkan hasil tertinggi yaitu A6 dengan nilai 0.935 atas nama sindi cantika yang memiliki pengalaman kerja 2 tahun dan keterampilan komunikasi nya sangat baik.
Data Mining Penerapan Asosiasi Apriori Dalam Penentuan Pola Penjualan Firdo Andri Saputra; Agus Iskandar
Journal of Computer System and Informatics (JoSYC) Vol 4 No 4 (2023): August 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v4i4.4043

Abstract

Penjualan merupakan hal yang tak terpisahkan dari kehidupan sehari-hari, di mana setiap orang secara rutin melakukan transaksi jual beli yang berdampak besar bagi pertumbuhan ekonomi. Bagi para pebisnis yang bergerak di bidang penjualan, seperti pemilik coffee shop, strategi untuk meningkatkan jumlah penjualan dan laba menjadi hal krusial. Dengan aktivitas transaksi harian yang terus meningkat, data penjualan tersebut bukan sekadar arsip biasa, melainkan memiliki potensi untuk diolah menjadi informasi berharga guna meningkatkan penjualan. Namun, seringkali terjadi masalah ketika stok item yang diinginkan oleh konsumen tidak tersedia atau habis karena kurangnya pengelolaan stok yang baik oleh karyawan coffee shop. Akibatnya, data transaksi penjualan yang ada hanya terabaikan dan tidak dimanfaatkan secara optimal, menyebabkan kerugian karena persediaan menu tidak terkontrol dengan baik. Sebaliknya, data transaksi sebelumnya dapat digunakan untuk mengidentifikasi pola pembelian pelanggan, sehingga dapat diambil kesimpulan bahwa jika pelanggan membeli menu A, kemungkinan besar akan membeli menu B juga. Untuk mengatasi permasalahan tersebut, solusi yang dapat diterapkan adalah memanfaatkan data transaksi penjualan yang telah terkumpul. Data ini mengandung informasi berharga yang bisa digunakan untuk pengambilan keputusan dan memperoleh pengetahuan baru tentang pola penjualan di coffee shop, sehingga pengelolaan persediaan menu dapat lebih terstruktur dan jumlah persediaan menu yang tidak diminati dapat dikurangi. Untuk mencapai persediaan yang ideal, peneliti menggunakan metode Data Mining, khususnya metode Assosiasi dengan Apriori. Namun, pencarian support hanya dilakukan hingga 3 set item tanpa ditemukan kombinasi yang memenuhi batas minimum support, sehingga proses tersebut dihentikan. Untuk melanjutkan pencarian nilai confidence, digunakan gabungan 2 set item (L2) yang memenuhi syarat untuk membentuk asosiasi. Hasil analisis dengan metode ini menghasilkan tiga aturan asosiasi yang berharga untuk meningkatkan penjualan, yaitu "Jika membeli cappuccino maka akan membeli donat" dengan confidence 0,65, "Jika membeli donat maka akan membeli cappuccino" dengan confidence 0,93, dan "Jika membeli muffin maka akan membeli cappuccino" dengan confidence 0,83.
Analisa Perbandingan Metode Teorema Bayes Dan Case Based Reasoning Diagnosa Penyakit Pada Tanaman Tomat Agus Iskandar; Galih Rakasiwi
Journal of Computer System and Informatics (JoSYC) Vol 5 No 1 (2023): November 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v5i1.4617

Abstract

Diseases in tomato plants have a significant impact on the agriculture industry as they can reduce crop yields and tomato quality. Therefore, this research aims to compare the Bayesian Theorem and Case-Based Reasoning (CBR) methods in diagnosing tomato plant diseases. The Bayesian Theorem is a statistical approach based on probability, while CBR uses knowledge from previous cases. This study includes an analysis of the performance of both methods in terms of diagnostic accuracy, result delivery speed, and resource efficiency. The research results have the potential to assist farmers and agricultural experts in choosing the most suitable method for diagnosing tomato plant diseases. Furthermore, the implementation of expert systems in agriculture can have a positive impact on tomato cultivation productivity and sustainability. This research aims to provide practical guidance for stakeholders in the agricultural field and contribute to sustainable agriculture improvement, with a specific focus on disease identification and management in tomato plants. The percentage values of the application of the Bayesian Theorem and Case-Based Reasoning methods show that Case-Based Reasoning has a lower success rate in diagnosing Fusarium Wilt and Bacterial Wilt compared to the Bayesian Theorem. However, Case-Based Reasoning excels in diagnosing Tomato Yellow Leaf Curl Virus (TYLCV), achieving a success rate of 100%, while the Bayesian Theorem reaches 63%.
Perbandingan Penggunaan Certainty Factor dan Pendekatan Dempster-Shafer dalam Sistem Expert untuk Mendiagnosis Kasus Cacar M. Mustaqim; Agus Iskandar
Journal of Computer System and Informatics (JoSYC) Vol 5 No 1 (2023): November 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v5i1.4618

Abstract

Varicella, another name for smallpox, is a viral infection that primarily affects children and can cause an itchy rash on the skin and fever. Smallpox should be diagnosed as soon as possible to stop the spread of the disease and provide appropriate treatment. The aim of this study was to compare the Dempster-Shafer and Certainty Factor (CF) approaches for diagnosing smallpox. The main aim of this study was to evaluate the effectiveness of both methods in detecting smallpox and to determine which is more accurate and reliable in the diagnosis process. The CF method is an approach in artificial intelligence that uses confidence factor values to describe an expert's level of confidence in an event or statement. Meanwhile, Dempster Shafer is a combined theory that can overcome uncertainty in decision making by modeling the level of confidence in various aspects. This research will outline the basic concepts of the Certainty Factor method and Dempster-Shafer Theory, as well as analyze their application when making a diagnosis of smallpox. The level of precision, dependability, and effectiveness of each technique will be compared. It is hoped that health professionals can improve smallpox diagnosis and make better clinical judgments with the help of the results of this study. The results of this research will help medical personnel and health practitioners make better decisions in diagnosing smallpox. Apart from that, this research can also help reduce diagnostic errors and speed up the treatment process. The calculation results from this research show that for Shingles, the Dempster Shafer approach produces a success rate of 86%, while the Certainty Factor method offers a success rate of 99%.
Co-Authors Aditya lutfi Irawan Aei Dhelwis Okdhianthy Putri Agung Triayudi Aldi Ardiansyah Amelia H Ikhlas Arie Gunawan Arya Dimas Setiadi Avicienna Adhi Narendro Purnomo badriawan sukma Bagas Prayoga Bimo Anggoro Seno Binsar Pamungkas Batu Bara Dandi Putra Denissa A. Putri Dhea Andarweni Eka Permana Putra Eldy Dwi Sentosa Fahmi Apriliansyah Fauziah Fauziah Firdo Andri Saputra Galih Rakasiwi Ghifari Yusuf Abdillah Haidar Ali Heru Saputro Iskandar Fitri Iskandar Fitri Iskandar Fitri Iskandar Fitri Iwan Wahyudin M. Iwan Wahyuddin M. Iwan Wahyudin M. Mustaqim Mauludani Muhammad Mochammad Arya Darmawan Moh. Iwan Wahyuddin Muhammad Naufal Rifqi Muhammad Rizky Hamdan Naufal Rifqi Niam Shofi Oky Triadi Sampurno Olipa Sarta Matilda Purba Puji Ariyanto Raihan Abdi Negoro Rian Rasyidhi Richard Ronaldo Marbun Rifqi Habibi Sachrrial Rifqi Habibi Sachrrial Rosyidah Rahmah Septi Andryana Shandy Nurjaman Sri Intan Maharani Ucuk Darusalam Vendy Blessing Gulo Yoga Septian Nugraha