Abstract:Eye diseases constitute a major health concern that may lead to visual impairment or blindness if not detected and treated at an early stage. Manual analysis of fundus images requires considerable time, accuracy, and ophthalmological…
thalmological expertise; therefore, a computational method capable of supporting automated and objective classification is needed. This study aims to develop a hybrid transfer learning and XGBoost model for classifying fundus images into four ocular condition categories. The methodology involved applying Contrast Limited Adaptive Histogram Equalization (CLAHE), resizing the images to 224 × 224 pixels, performing retina-specific augmentation on the training data, and stratifying the dataset into training, validation, and testing sets. An ImageNet-pretrained EfficientNetV2S model was employed through transfer learning and fine-tuning to extract image features. The resulting 256-dimensional embeddings, model output probabilities, and statistical image features were subsequently combined, standardized, and classified using XGBoost optimized through five-fold cross-validation. The dataset comprised 4,217 fundus images distributed across four classes: 1,038 cataract images, 1,098 diabetic retinopathy images, 1,007 glaucoma images, and 1,074 normal images, including 2,949 training, 631 validation, and 637 testing images. Evaluation on the 637 test images demonstrated that the proposed transfer learning–XGBoost model achieved an accuracy of 92.78%, macro-precision of 0.93, macro-recall of 0.93, macro F1-score of 0.9265, and one-vs-rest ROC-AUC of 0.9871. The proposed model outperformed the standalone EfficientNetV2S model, which achieved an accuracy of 90.27%, by 2.51 percentage points, with class-specific F1-scores of 0.96 for cataract, 0.99 for diabetic retinopathy, 0.87 for glaucoma, and 0.88 for normal. These findings demonstrate that integrating transfer learning with XGBoost effectively improves eye disease classification and has the potential to support early identification using fundus images, although it is not intended to replace clinical diagnosis by ophthalmologists.
Abstract:Abstract: The Digital Population Identity (IKD) application allows citizens to access population documents electronically. Service evaluations can be found in user reviews on the Google Play Store, but manual analysis is…
challenging due to their large and unstructured volume. This study analyzes the sentiment of IKD user reviews and compares the effectiveness of Support Vector Machine (SVM) and Random Forest. Reviews were collected through web scraping, labeled positive or negative by specialists, and preprocessed (cleaning, case folding, tokenizing, normalization, stopword removal, stemming) before being converted into numerical features using TF-IDF weighting. The weighted features were grouped using K-Means clustering to map sentiment tendencies prior to classification. SVM classified sentiment by identifying a maximum-margin hyperplane across classes, while Random Forest combined the predictions of numerous decision trees through majority voting. Both models were trained and tested with a 70:30 stratified split and evaluated using a confusion matrix. From 9,243 scraped reviews collected between June 2025 and June 2026, 5,714 remained after cleaning. SVM achieved 93.64% accuracy with weighted precision, recall, and F1-score of 0.94, while Random Forest achieved 93.41% accuracy with an F1-score of 0.93. Sentiment was dominated by the negative class at 75.5%, mainly complaints about application performance. These results provide a data-driven basis for the Directorate General of Population and Civil Registration to prioritize feature improvements and enhance IKD service quality.
Keywords: sentiment analysis; IKD; SVM; Random Forest
Abstrak: Aplikasi Identitas Kependudukan Digital (IKD) memungkinkan masyarakat mengakses dokumen kependudukan secara elektronik. Ulasan pengguna di Google Play Store memuat penilaian layanan, namun jumlahnya besar dan tidak terstruktur sehingga sulit dianalisis secara manual. Penelitian ini menganalisis sentimen ulasan pengguna IKD dan membandingkan kinerja Support Vector Machine (SVM) dengan Random Forest. Data ulasan dikumpulkan melalui web scraping, diberi label positif dan negatif oleh pakar, lalu dipraproses (cleaning, case folding, tokenizing, normalisasi, stopword removal, stemming) sebelum diubah menjadi fitur numerik dengan pembobotan TF-IDF. Fitur hasil pembobotan dikelompokkan dengan K-Means Clustering untuk memetakan kecenderungan sentimen sebelum klasifikasi. SVM mengklasifikasikan sentimen dengan mencari hyperplane bermargin maksimum antar kelas, sedangkan Random Forest menggabungkan prediksi banyak decision tree melalui majority voting. Kedua model dilatih dan diuji dengan pembagian data 70:30 stratified serta dievaluasi menggunakan confusion matrix. Dari 9.243 ulasan hasil scraping pada rentang Juni 2025 sampai Juni 2026, tersisa 5.714 ulasan setelah pembersihan. SVM memperoleh akurasi 93,64% dengan precision, recall, dan F1-score tertimbang 0,94, sedangkan Random Forest memperoleh akurasi 93,41% dengan F1-score 0,93. Sentimen didominasi kelas negatif sebesar 75,5%, terutama keluhan pada kinerja aplikasi. Hasil penelitian ini menjadi dasar evaluasi berbasis data bagi Direktorat Jenderal Kependudukan dan Pencatatan Sipil untuk memprioritaskan perbaikan fitur dan meningkatkan kualitas layanan IKD.
Kata kunci: analisis sentimen; IKD; SVM; Random Forest
Abstract:Abstract: This study investigates the optimization of Human Resources (HR) through the adoption of digital technology in public service and agricultural MSME operations within Pematang Bandar Subdistrict, Simalungun Regency.…
ncy. The research focuses on the pivotal role of digital literacy and technology-based training in driving work efficiency and service quality. Utilizing a qualitative descriptive approach grounded in the Technology Acceptance Model (TAM) and Human Resource Development (HRD) theories, primary data were gathered through in-depth interviews and field observations involving subdistrict administrative staff, local MSME actors, and community leaders. The findings reveal that while technological infrastructure has been introduced, the primary bottleneck in digital transformation lies in varying levels of HR digital literacy and resistance to technological change. Implementing structured, ongoing digital training combined with continuous technical mentoring significantly enhances HR readiness, reduces operational delays, and improves service delivery. This study highlights the necessity of strategic HR technology integration and tailored capacity-building programs to achieve sustainable regional development in rural-urban transition areas such as Pematang Bandar.
Keywords: Human Resources, Digital Technology Adoption, Digital Literacy, HR Training, Pematang Bandar, TAM.
Abstrak: Penelitian ini bertujuan untuk menganalisis optimasi Sumber Daya Manusia (SDM) melalui adopsi teknologi digital pada instansi pelayanan publik dan pelaku UMKM di Kecamatan Pematang Bandar, Kabupaten Simalungun. Fokus utama penelitian ini tertuju pada peran penting literasi teknologi dan pelatihan berbasis digital dalam meningkatkan efisiensi kerja serta kualitas pelayanan. Menggunakan pendekatan kualitatif deskriptif yang dilandasi kerangka Technology Acceptance Model (TAM) dan pengembangan SDM, data primer dikumpulkan melalui wawancara mendalam dan observasi lapangan bersama aparatur kecamatan, pelaku usaha lokal, serta tokoh masyarakat Pematang Bandar. Hasil penelitian menunjukkan bahwa kendala utama transformasi digital di daerah ini bukan hanya ketersediaan infrastruktur, melainkan ketimpangan tingkat literasi digital SDM dan adanya resistensi terhadap perubahan sistem kerja konvensional. Pelaksanaan pelatihan digital yang terstruktur dan pendampingan teknis secara berkelanjutan terbukti secara signifikan meningkatkan kesiapan SDM, memangkas birokrasi operasional, serta mengoptimalkan kualitas pelayanan publik. Studi ini menyimpulkan pentingnya integrasi strategi teknologi SDM yang inklusif guna mendorong pembangunan daerah yang berkelanjutan di Pematang Bandar.
Kata kunci: Sumber Daya Manusia, Adopsi Teknologi Digital, Literasi Digital, Pelatihan SDM, Pematang Bandar, TAM.
Abstract:Abstract: This study investigates the strategic management of media relations by Public Relations (PR) practitioners in Badan Usaha Milik Daerah (BUMD) during operational crises, focusing specifically on the pivotal role…
of Forum Wartawan Tirtanadi (Forwadi) in shaping and sustaining public sentiment. Employing a qualitative descriptive approach grounded in Situational Crisis Communication Theory (SCCT), primary data were gathered through structured, in-depth interviews with PR executives, operational leaders, and media partners at PDAM Tirtanadi Medan. The study addresses the critical challenge of information fragmentation and public anxiety stemming from service disruptions. Findings demonstrate that PDAM Tirtanadi mitigates negative public sentiment through a multi-tiered media relations strategy: executing rapid field-data verification, establishing transparent digital communication hubs via specialized journalist forums, and leveraging local community networks alongside broadcast media. The integration of structured media partnerships with empirical, photo-documented field reporting significantly diminishes public skepticism and stabilizes organizational trust during critical service outages. The research underscores the imperative for continuous crisis communication training and real-time digital integration between field operations and PR departments in public service enterprises.
Keywords: Media Relations, Crisis Communication, Forum Wartawan, Public Sentiment, BUMD, SCCT
Abstrak: Penelitian ini bertujuan untuk menginvestigasi secara mendalam manajemen media relations yang dijalankan oleh Public Relations (PR) Badan Usaha Milik Daerah (BUMD) dalam situasi krisis operasional, dengan fokus utama pada peran strategis Forum Wartawan Tirtanadi (Forwadi) dalam mengelola dan menjaga sentimen publik. Menggunakan pendekatan kualitatif deskriptif yang dilandasi kerangka Situational Crisis Communication Theory (SCCT), data primer dikumpulkan melalui wawancara mendalam terstruktur bersama praktisi PR, pimpinan unit operasional, serta jurnalis mitra di PDAM Tirtanadi Medan. Penelitian ini menyoroti tantangan utama berupa fragmentasi informasi dan keresahan publik akibat gangguan distribusi air. Hasil penelitian menunjukkan bahwa PDAM Tirtanadi meredam sentimen negatif publik melalui strategi media relations bertingkat: verifikasi data teknis lapangan secara cepat, pembentukan pusat komunikasi digital yang transparan melalui forum wartawan khusus, serta pemanfaatan jejaring komunitas lokal dan media penyiaran. Integrasi antara kemitraan media yang terstruktur dan pelaporan berbasis bukti faktual di lapangan terbukti secara signifikan menekan spekulasi publik dan memulihkan kepercayaan terhadap BUMD. Studi ini menyimpulkan pentingnya pelatihan komunikasi krisis secara berkelanjutan serta integrasi data real-time antara tim operasional dan tim PR pada BUMD pelayanan publik.
Kata kunci: Media Relations, Krisis Komunikasi, Forum Wartawan, Sentimen Publik, BUMD, SCCT
Abstract:Abstract: Falls represent a leading cause of injury-related mortality among elderly individuals, demanding reliable automated detection deployable in resource-constrained environments. Existing vision-based approaches predominantly…
edominantly employ machine learning classifiers requiring large labelled datasets, substantial training pipelines, and GPU-class hardware, impeding rapid clinical deployment. This paper presents an explainable, fully rule-based real-time fall detection system implemented in Python using MediaPipe Pose, designed to operate on commodity edge devices without any training dataset. The system comprises three tightly integrated layers: (1) a mathematical formulation layer computing vector-based torso angle, distance-ratio body normalization, and frame-to-frame CoM velocity; (2) a temporal modeling layer combining a deterministic four-state Finite State Machine with a sliding-window motion energy analysis; and (3) a robustness evaluation across three camera viewing angles, two lighting conditions, and three fall types. Experimental validation yields a sensitivity of 91.8%, specificity of 95.8%, and F1-score of 93.1%, with a mean end-to-end detection latency of 140.1 ms on Raspberry Pi 4—satisfying a 200 ms real-time constraint while consuming only 72.5% mean CPU and 284 MB RAM. The rule-based architecture provides full decision auditability without black-box models, aligning with emerging XAI requirements for medical device regulation.
Keywords: Periodic Table of Elements; Chemistry Teaching Materials; Spiritual Values; Discovery Learning.
Abstrak: Kondisi Jatuh merupakan penyebab utama kematian akibat cedera di kalangan lansia, sehingga membutuhkan deteksi otomatis yang andal dan dapat diterapkan di lingkungan dengan keterbatasan sumber daya. Pendekatan berbasis visi yang ada sebagian besar menggunakan pengklasifikasi pembelajaran mesin yang membutuhkan kumpulan data berlabel besar, alur pelatihan yang substansial, dan perangkat keras kelas GPU, yang menghambat penerapan klinis yang cepat. Makalah ini menyajikan sistem deteksi jatuh waktu nyata yang dapat dijelaskan dan sepenuhnya berbasis aturan yang diimplementasikan dalam Python menggunakan MediaPipe Pose, yang dirancang untuk beroperasi pada perangkat edge komoditas tanpa kumpulan data pelatihan apa pun. Sistem ini terdiri dari tiga lapisan yang terintegrasi erat: (1) lapisan formulasi matematika yang menghitung sudut tubuh berbasis vektor, normalisasi tubuh rasio jarak, dan kecepatan CoM antar frame; (2) lapisan pemodelan temporal yang menggabungkan Mesin Keadaan Terbatas empat keadaan deterministik dengan analisis energi gerak jendela geser; dan (3) evaluasi ketahanan di tiga sudut pandang kamera, dua kondisi pencahayaan, dan tiga jenis jatuh. Validasi eksperimental menghasilkan sensitivitas 91,8%, spesifisitas 95,8%, dan skor F1 93,1%, dengan latensi deteksi ujung-ke-ujung rata-rata 140,1 ms pada Raspberry Pi 4—memenuhi batasan waktu nyata 200 ms sambil hanya mengonsumsi 72,5% CPU rata-rata dan 284 MB RAM. Arsitektur berbasis aturan memberikan kemampuan audit keputusan penuh tanpa model kotak hitam, selaras dengan persyaratan XAI yang muncul untuk regulasi perangkat medis.
Kata Kunci: Deteksi jatuh, MediaPipe Pose, komputasi tepi, sistem berbasis aturan, kecerdasan buatan dengan keadaan terbatas, AI yang dapat dijelaskan, perawatan lansia, pemantauan real-time
Abstract:Abstract: This study aims to identify, evaluate, and synthesize empirical evidence regarding the role of gold as a safe-haven, hedge, and diversifier instrument in modern portfolios, employing a Systematic Literature Review…
iew (SLR) approach within the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework. Articles were analyzed using thematic synthesis, involving a coding process based on crisis characteristics, econometric methods, and market types. Out of 309 articles identified across the Scopus, Web of Science, ScienceDirect, and Google Scholar databases, 20 met all inclusion criteria for the 2020–2026 period. The synthesis results indicate that gold's effectiveness as a safe haven is conditional and dynamic rather than universal and static: gold proved effective in protecting portfolio value during structural financial crises—such as the 2008 Global Financial Crisis—and prolonged geopolitical tensions like the Russia-Ukraine conflict, yet it lost its protective function during the initial phases of sudden liquidity crises, such as the COVID-19 pandemic. Key moderating factors identified include crisis typology, gold market volatility, interest rate policies, exchange rate dynamics, and market characteristics—including structural differences between developed markets and emerging markets like Indonesia. The emergence of crypto-assets has not fully displaced gold's position; instead, it creates opportunities for strategic complementarity that can enhance risk-adjusted returns in modern portfolio construction.
Keywords: safe haven; gold; systematic literature review; portfolio management; cryptocurrency
Abstrak: Penelitian ini bertujuan mengidentifikasi, mengevaluasi, dan mensintesis bukti empiris mengenai peran emas sebagai instrumen safe haven, hedge, dan diversifier dalam portofolio modern melalui pendekatan Systematic Literature Review (SLR) dengan kerangka Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). Artikel dianalisis menggunakan thematic synthesis dengan proses coding berdasarkan karakteristik krisis, metode ekonometrika, serta tipe pasar. Dari 309 artikel yang diidentifikasi melalui basis data Scopus, Web of Science, ScienceDirect, dan Google Scholar, sebanyak 20 artikel memenuhi seluruh kriteria inklusi untuk rentang periode 2020–2026. Hasil sintesis menunjukkan bahwa efektivitas emas sebagai safe haven bersifat kondisional dan dinamis, bukan universal dan statis: emas terbukti efektif melindungi nilai portofolio dalam konteks krisis finansial struktural seperti Global Financial Crisis 2008 serta tekanan geopolitik berkepanjangan seperti konflik Rusia–Ukraina, namun kehilangan fungsi protektifnya selama fase awal krisis likuiditas mendadak seperti pandemi Covid-19. Faktor-faktor moderasi utama yang teridentifikasi mencakup tipologi krisis, tingkat volatilitas pasar emas itu sendiri, kebijakan suku bunga, dinamika nilai tukar, serta karakteristik pasar, termasuk perbedaan struktural antara pasar maju dan emerging market seperti Indonesia. Kehadiran aset kripto tidak sepenuhnya menggantikan posisi emas, melainkan membentuk peluang komplementaritas strategis yang dapat meningkatkan risk-adjusted return dalam konstruksi portofolio modern.
Kata kunci: safe haven; emas; systematic literature review; manajemen portofolio; cryptocurrency
Abstract:Abstract: Student dropout has become a significant challenge for higher education institutions because it negatively affects academic performance, institutional reputation, and resource allocation. Early identification of…
f students at risk of dropping out enables universities to implement timely interventions and improve student retention. This study proposes a predictive model for identifying students at risk of dropping out using the Extreme Gradient Boosting (XGBoost) algorithm based on academic data. The dataset consists of student academic records, including grade point average (GPA), course completion rate, attendance, accumulated credits, failed courses, and semester performance. The data were preprocessed through data cleaning, feature selection, and normalization to improve model performance. The dataset was then divided into training and testing sets using an 80:20 ratio. The XGBoost model was trained and evaluated using accuracy, precision, recall, F1-score, and Area Under the Receiver Operating Characteristic Curve (AUC-ROC). The experimental results demonstrate that XGBoost effectively captures complex relationships among academic variables and provides high predictive performance in identifying students with dropout risk. Feature importance analysis further reveals that GPA, accumulated credits, attendance rate, and the number of failed courses are the most influential factors affecting student dropout. The proposed approach offers a practical decision-support tool for higher education institutions by enabling early detection of at-risk students and facilitating targeted academic support programs. The findings contribute to the application of machine learning in educational data mining and learning analytics, providing valuable insights for improving student retention strategies and academic success. Future research may integrate non-academic factors such as socioeconomic background, psychological characteristics, and student engagement to further enhance prediction accuracy and model generalizability across different educational institutions.
Keywords: Student Dropout Prediction; XGBoost; Machine Learning; Educational Data Mining; Learning Analytics.
Abstrak: Putus kuliah merupakan tantangan signifikan bagi institusi pendidikan tinggi karena berdampak negatif terhadap kinerja akademik, reputasi institusi, dan alokasi sumber daya. Identifikasi dini terhadap mahasiswa yang berisiko putus kuliah memungkinkan universitas untuk menerapkan intervensi yang tepat waktu dan meningkatkan retensi mahasiswa. Penelitian ini mengusulkan model prediktif untuk mengidentifikasi mahasiswa yang berisiko putus kuliah menggunakan algoritma Extreme Gradient Boosting (XGBoost) berdasarkan data akademik. Kumpulan data mencakup rekam jejak akademik mahasiswa, termasuk Indeks Prestasi Kumulatif (IPK), tingkat penyelesaian mata kuliah, kehadiran, akumulasi kredit, mata kuliah yang gagal, dan kinerja semester. Data diproses melalui tahapan pembersihan data, seleksi fitur, dan normalisasi untuk meningkatkan kinerja model. Selanjutnya, kumpulan data dibagi menjadi set pelatihan dan pengujian dengan rasio 80:20. Model XGBoost dilatih dan dievaluasi menggunakan metrik akurasi, presisi, recall, skor-F1, dan Area Under the Receiver Operating Characteristic Curve (AUC-ROC). Hasil eksperimen menunjukkan bahwa XGBoost secara efektif menangkap hubungan kompleks antarvariabel akademik dan memberikan kinerja prediksi yang tinggi dalam mengidentifikasi mahasiswa dengan risiko putus kuliah. Analisis kepentingan fitur mengungkapkan bahwa IPK, akumulasi kredit, tingkat kehadiran, dan jumlah mata kuliah yang gagal merupakan faktor paling berpengaruh terhadap risiko putus kuliah mahasiswa. Pendekatan yang diusulkan ini menawarkan alat pendukung keputusan yang praktis bagi institusi pendidikan tinggi dengan memungkinkan deteksi dini mahasiswa berisiko serta memfasilitasi program dukungan akademik yang terarah. Temuan ini berkontribusi pada penerapan machine learning dalam penambangan data pendidikan dan analitik pembelajaran, serta memberikan wawasan berharga untuk meningkatkan strategi retensi mahasiswa dan keberhasilan akademik. Penelitian di masa mendatang dapat mengintegrasikan faktor non-akademik—seperti latar belakang sosial-ekonomi, karakteristik psikologis, dan keterlibatan mahasiswa—untuk lebih meningkatkan akurasi prediksi dan kemampuan generalisasi model di berbagai institusi pendidikan.
Kata kunci: Prediksi Putus Kuliah Mahasiswa; XGBoost; Machine Learning; Penambangan Data Pendidikan; Analitik Pembelajaran.
Abstract:Abstract: This article explores the communication crisis challenges faced by PDAM Tirtanadi and the role of Public Relations (PR) in managing these issues to improve organizational performance. This study employs a qualitative…
tative approach with in-depth interviews with the company staff, as the primary data collection method. The findings reveal that the main challenges include a lack of transparency and consistency in delivering information to the public. To address these issues, PDAM Tirtanadi has developed various strategies, such as strengthening internal communication, fostering good relationships with the media through the Forum Wartawan Tirtanadi (Forwadi), and utilizing digital technology for information dissemination. The study concludes by highlighting the importance of crisis communication training for PR teams, strengthening internal communication systems, and ensuring transparency in public interactions to maintain public trust and enhance the organization's reputation.
Keywords: Public Relations, Crisis Communication, Organization Performance
Abstrak: Penelitian ini bertujuan untuk mengetahui komunikasi krisis yang dihadapi oleh PDAM Tirtanadi dan menganalisis peran Public Relations (PR) dalam mengelola situasi tersebut untuk meningkatkan kinerja organisasi. Penelitian ini menggunakan pendekatan kualitatif dengan wawancara mendalam dengan pihak PT sebagai metode pengumpulan data utama. Hasil penelitian menunjukkan bahwa tantangan utama dalam menghadapi krisis komunikasi adalah kurangnya transparansi dan konsistensi dalam penyampaian informasi kepada masyarakat. Untuk mengatasi hal ini, PDAM Tirtanadi telah mengembangkan berbagai strategi, seperti memperkuat komunikasi internal, menjalin hubungan baik dengan media melalui Forum Wartawan Tirtanadi (Forwadi), dan memanfaatkan teknologi digital dalam menyampaikan informasi. Kesimpulan dari penelitian ini menunjukkan bahwa pentingnya pelatihan komunikasi krisis bagi tim PR, penguatan sistem komunikasi internal, serta transparansi dalam berinteraksi dengan masyarakat untuk menjaga kepercayaan publik dan meningkatkan reputasi organisasi.
Kata kunci: Public Relations, Krisis Komunikasi, Kinerja Organisasi
Abstract:This study aims to determine and analyze the effect of time management on employee work productivity at Elim Rantepao Hospital. The primary issue addressed is operational bottlenecks, such as delayed task completion due…
to sub-optimal time allocation. The research method employed is quantitative with a causal associative approach. The population consists of 500 employees, with a sample size of 83 respondents determined via the Slovin formula and selected using simple random sampling. Data were gathered through Likert-scale questionnaires and analyzed using simple linear regression Analysis with SPSS version 25. The hypothesis test results (t-test) show a value of 8.002, which is greater than the value of 1.664, with a significance level of , thereby accepting . This proves that time management has a positive and significant effect on employee work productivity. The coefficient of determination (R Square) of 0.442 indicates that time management contributes 44.2% to work productivity, while the remaining 55.8% is influenced by external variables. The management is recommended to implement time management training and conduct regular workload evaluations.
Abstract:This study aims to analyze the effect of recruitment and training on employee performance, both directly and indirectly through job satisfaction as an intervening variable at BPKPD Tana Toraja Regency. Utilizing a quantitative…
tative method with a causal associative approach, this study applied a saturated sampling technique involving the entire active ASN population of 61 respondents. Primary data were gathered using Likert-scaled structured questionnaires and analyzed through Path Analysis with SPSS 25. The simultaneous F-test results prove that recruitment, training, and job satisfaction collectively have a significant effect on employee performance (). However, the partial t-test results indicate that in the full model, neither recruitment (; ) nor training (; ) has a direct significant effect on employee performance. The contributions of both independent variables are fully and perfectly mediated (full mediation) by job satisfaction, which records a positive and most dominant effect on performance (; ). It is concluded that accelerating employee performance highly depends on optimizing the apparatus's psychological satisfaction through appropriate task placement and more relevant training programs.