A Hybrid Approach Using K-Means Clustering and the SAW Method for Evaluating and Determining the Priority of SMEs in Palembang City

The current efforts to develop Small and Medium Enterprises (SMEs) are still facing challenges in setting appropriate targets. Although the Palembang City Cooperative and SME Agency has launched various programs and initiatives to support SME development, they have not yet successfully identified the SMEs that should be given priority for development. This study aims to apply a hybrid approach that combines the K-Means Clustering method and Simple Additive Weighting (SAW) to evaluate and prioritize SME development in Palembang City. The K-Means Clustering method is used to group SMEs based on their characteristics, while SAW provides preference values ( ). The SME data was obtained from the Palembang City Cooperative and SME Agency, covering 362 SME units. The K-Means Clustering results yielded two clusters: Cluster 0 as the High Growth Cluster and Cluster 1 as the Stability and Improvement Cluster. Validation using cross-validation showed that this model achieved an accuracy of 99.72%. The SAW analysis on Cluster 0 indicated that the Kopi Kaljo SME received the highest priority with a preference value of 45.71. This study confirms that this hybrid approach is effective in grouping SMEs based on their characteristics and prioritizing them based on data-driven evaluation. The research results are expected to help the Palembang City Cooperative and SME Agency design more effective and targeted assistance programs to optimize the contribution of SMEs to local economic growth to the maximum extent

A Hybrid Approach Using K-Means Clustering and the SAW Method for Evaluating and Determining the Priority of SMEs in Palembang City

The current efforts to develop Small and Medium Enterprises (SMEs) are still facing challenges in setting appropriate targets. Although the Palembang City Cooperative and SME Agency has launched various programs and initiatives to support SME development, they have not yet successfully identified the SMEs that should be given priority for development. This study aims to apply a hybrid approach that combines the K-Means Clustering method and Simple Additive Weighting (SAW) to evaluate and prioritize SME development in Palembang City. The K-Means Clustering method is used to group SMEs based on their characteristics, while SAW provides preference values ( ). The SME data was obtained from the Palembang City Cooperative and SME Agency, covering 362 SME units. The K-Means Clustering results yielded two clusters: Cluster 0 as the High Growth Cluster and Cluster 1 as the Stability and Improvement Cluster. Validation using cross-validation showed that this model achieved an accuracy of 99.72%. The SAW analysis on Cluster 0 indicated that the Kopi Kaljo SME received the highest priority with a preference value of 45.71. This study confirms that this hybrid approach is effective in grouping SMEs based on their characteristics and prioritizing them based on data-driven evaluation. The research results are expected to help the Palembang City Cooperative and SME Agency design more effective and targeted assistance programs to optimize the contribution of SMEs to local economic growth to the maximum extent

Sistem Penilaian Kinerja Dosen menggunakan Decision Maker Respondent Opinion Model

The subjectiveness of the performance assessment can not be avoided. The criteria determined for the performance assessment with human cognition is the subjectiveness phenomenon.  This condition is being a challenge for the researcher to develop the performance assessment technique objectively.  This method is a new approach to determine the performance assessment indicator with involving the employee to result in the realistic assessment.  This model utilized the respondent opinion as a decision-maker to determine the relevance criteria and sub-criteria in the target achievement.  The lecturer performance assessment is designed by utilizing the Decision-maker respondent opinion model (DMROM) algorithm. It is applicable to the criteria which contain the subjectiveness on the unlimited level.  The data is arranged in the encoded item table to make it easy for the process of the calculation and sub-criteria which reach the support minimum limit. The algorithm is utilized the simple formula which could count the weight of each criterion and sub-criteria to the last level. The weight value on each sub-criteria will be accumulated as a sign of doing activities. The final result of this model is the list of the lecturer performance assessment who fulfilled the target or not as an input for the leader to take the decision.Keywords:  Opini responden, Decision maker respondent opinion model, performance appraisal  Subjektivitas pada penilaian kinerja tidak dapat dihindarkan. Penetapan kriteria untuk penilaian kinerja dengan kognisi manusia merupakan fenomena subjektif. Hal ini menjadi tantangan bagi peneliti untuk mengembangkan teknik penilaian kinerja secara objektif. Cara ini merupakan pendekatan baru untuk menentukan indikator penilaian kinerja dengan melilbatkan karyawan (responden) agar menghasilkan penilaian yang lebih realistis. Model ini menggunakan opini responden sebagai pengambil keputusan untuk menentukan kriteria dan subkriteria yang relevan dalam pencapaian target. Penilaian kinerja dosen dibangun dengan menggunakan algoritma Decision maker respondent opinion model (DMROM). Algoritma DMROM mampu diterapkan pada kriteria yang memiliki subkriteria pada level tak terbatas. Data disusun dalam Encoded item table untuk memudahkan proses perhitungan dan seleksi subkriteria yang mencapai batas minimum support. Algoritma ini juga menggunakan rumus sederhana yang dapat menghitung bobot pada masing-masing kriteria dan subkriteria di level ke-n. Nilai bobot pada masing-masing subkriteria akan diakumulasikan sebagai tanda melaksanakan kegiatan. Hasil akhir model ini adalah daftar penilaian kinerja dosen yang memenuhi target atau tidak sebagai masukan  bagi pimpinan  untuk mengambil keputusan.  Kata Kunci :  Opini responden, Decision maker respondent opinion model, Penilaian kinerja  

ANALISA PENERAPAN SISTEM INFORMASI PERPUSTAKAAN UNIVERSITAS INDO GLOBAL MANDIRI

Increasing student achievement is not only dependent on science is taught by lecturers, but with a high interest, will be able to broaden their field of study miliki.Untuk increase reading interest of students, a university must have a good library information system. Library is one of the important unit in the establishment of a college. Library information system supported by good data processing, SOP good and also good infrastructure (standard), will have a significant impact on the progressive increase in interest in reading Mahasiswa.Suatu universities that have implemented the library information system, should be able to ensure that the systems supporting applications information library, actually still running well. Measurement of whether or not the system, carried out by analyzing the indicators - indicators of success of the system. Indicators of success is measured by the method menggunan COBIT Framework. COBIT Framework has four domains. The domain used to measure library information system is the delivery and support (delivery and Support). By calculating the maturity value, it can be determined whether good or bad condition of the Library Information System. The case study of this research takes the object of study in the University of Indo Global Mandiri. The results of this study, researchers in the form of an analysis of the library information system UIGM today, and suggestions for the leadership of Higher Education for the development of library information system better, in order to meet the needs of the organization and its stakeholders

EVALUASI PEMANFAATAN TATA KELOLA TEKNOLOGI INFORMASI DAN KOMUNIKASI (TIK) PADA PENERAPAN E-KTP MENGGUNAKAN FRAMEWORK COBIT

Pemanfaatan teknologi informasi dan komunikasi oleh institusi pemerintahan telah semakin meningkat, sehingga perlu memastikan pemanfaatan  teknologi informasi dan komunikasi tersebut benar-benar mendukung tujuan penyelenggaraan pemerintahan.  Teknologi Informasi dan Komputer (TIK) yang sedang diterapkan pemerintah dalam meningkatkan pelayanan publik saat ini adalah eKTP. Untuk memastikan penggunanan TIK tersebut dapat diterapkan dengan maksimal diperlukan Good Governance  yang dihubungkan  dengan TIK yang disebut dengan Tata Kelola TIK.  Salah satu kerangka kerja tatakelola TI adalah CobiT. Dalam dokumentasi resminya CobiT juga disertai dengan serangkaian pedoman seperti pedoman manajemen dan pedoman implementasi. Pedoman implementasi menyediakan serangkaian alat dan tahapan untuk mengimplementasikan tatakelola berdasarkan kerangka kerja CobiT yang meliputi elemen pengukuran kerja, daftar factor keberhasilan kritis dan pengukuran tingkat kematangan (maturity). Semua alat tersebut dirancang untuk mendukung keberhasilan implementasi tata kelola pada berbagai obyek pengendalian (control objective) di bidang TI.  Kata kunci : Tata Kelola, Teknologi Informasi dan Komputer (TIK), CobiT, Maturity Mode

Predictive Buyer Behavior Model as Customer Retention Optimization Strategy in E-commerce

Lazada is one of the rapidly growing E-commerce platforms in this digital era. One of the main challenges faced by Lazada is customer retention, where customers make purchases once or a few times before switching to other platforms. Therefore, it is important to understand buyer behavior in E-commerce through customer prediction to identify factors influencing retention. This study employs the Random Forest (RF) method to analyze Lazada customer data and formulate more effective marketing strategies. The analysis is conducted by loading preprocessed datasets into the KNIME workflow and utilizing various nodes and algorithms available in KNIME to build and evaluate predictive models. The Random Forest model is trained multiple times to achieve the highest Accuracy rate, which is 72.472%, with a fairly high level of agreement and a balanced trade-off between recall and precision. Additionally, this model successfully predicts that customers purchasing electronic equipment are potentially churning at a rate of 3.85%. Subsequently, customer strategy analysis for customer retention optimization in the E-commerce industry is conducted through data visualization using Tableau. Predictive analysis of customer behavior serves as a strong foundation for formulating effective retention strategies in the E-commerce industry. With this approach, Lazada can enhance customer experience and ensure sustainability in facing the increasingly fierce competition in the digital market

Predictive Buyer Behavior Model as Customer Retention Optimization Strategy in E-commerce

Lazada is one of the rapidly growing E-commerce platforms in this digital era. One of the main challenges faced by Lazada is customer retention, where customers make purchases once or a few times before switching to other platforms. Therefore, it is important to understand buyer behavior in E-commerce through customer prediction to identify factors influencing retention. This study employs the Random Forest (RF) method to analyze Lazada customer data and formulate more effective marketing strategies. The analysis is conducted by loading preprocessed datasets into the KNIME workflow and utilizing various nodes and algorithms available in KNIME to build and evaluate predictive models. The Random Forest model is trained multiple times to achieve the highest Accuracy rate, which is 72.472%, with a fairly high level of agreement and a balanced trade-off between recall and precision. Additionally, this model successfully predicts that customers purchasing electronic equipment are potentially churning at a rate of 3.85%. Subsequently, customer strategy analysis for customer retention optimization in the E-commerce industry is conducted through data visualization using Tableau. Predictive analysis of customer behavior serves as a strong foundation for formulating effective retention strategies in the E-commerce industry. With this approach, Lazada can enhance customer experience and ensure sustainability in facing the increasingly fierce competition in the digital market