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Pengaruh Price to Book Value, ROA, CR dan DER Terhadap Harga Saham
Get PDFTemuan dalam penelitian ini dilakukan guna mendapatkan dampak antara variabel independen seperti pbv, roa, cr dan der akan harga saham. Terutama bagaimana harga saham per lembar saham dibagi dengan book value, laba bersih dibagi dengan semua asset, aset lancar dibagi dengan utang lancar dan total utang dibagi total modal serta harga saham pada maskapai kendaraan tercantum di bursa efek Indonesia, individual atau kolektif ada hubungannya. Metode analisis yang dipergunakan dengan kuantitatif, deskriptif serta verifikatif, dengan jumlah lbersamaaporan keuangan yang dipergunakan berjumlah 48 perusahaan transportasi dari tahun 2017 hingga 2024. Temuan pada studi ini mengungkapkan bahwa secara individual, setiap variabel independen secara signifikan memberi dampak terhadap harga saham. Begitupula, dampak yang dihasilkan secara kolektif, variabel-variabel independen ini menunjukkan efek gabungan yang signifikan, hingga sebesar 59,1% tingkat hubungan yang dinilai sedan
Factors Influencing Labor Absorption in the Micro and Small Industry Sector in Banten Province for the 2019-2023 Period
Get PDFThis research seeks to investigate how the quantity of industries, Gross Regional Domestic Product, Production Value, Minimum Wage affects Labor Absorption in the micro and small industry sector in Banten Province. With 8 regencies and cities in Banten and a research timeframe from 2019 to 2023 utilizing quantitative methods, the analysis employing multiple linear regression demonstrates that the model possesses exceptional explanatory power. The R-squared value of 0.989332 reveals that approximately 98.93% of the variation in the dependent variable (labor force number/Y) is accounted for by the independent variables. According to the findings of the regression analysis, the Gross Domestic Product (GDP) variable positively affects the level of employment (labor). This suggests that there is statistically inadequate evidence to claim that alterations in the number of industries, productivity levels, or minimum wage directly influence labor absorption levels. This condition could arise from factors beyond the primary model, or the third variable's influence may be of an indirect nature. This model indicates that GDP could be a key element affecting labor absorption, whereas other factors have not yet played a substantial role
Adopsi Digital Payment ZIS oleh Generasi Z: Apakah Financial Technology Meningkatkan Niat Membayar Zakat, Infaq dan Sodaqoh?
Get PDFPenelitian ini bertujuan untuk mengkaji faktor-faktor yang memotivasi Generasi Z dalam menggunakan metode pembayaran digital secara konsisten untuk penyaluran Zakat, Infak, dan Sedekah (ZIS). Studi ini berfokus pada determinan utama dari niat perilaku (behavioral intention/BI) Generasi Z, yang mencakup kesiapan adopsi (adoption readiness/AR), persepsi risiko (perceived risk/PR), kepercayaan (trust/TR), dan inovasi personal (personal innovativeness/PI). Kebaruan dari penelitian ini terletak pada fokusnya terhadap preferensi Generasi Z dalam penggunaan pembayaran digital untuk ZIS, dengan mempertimbangkan integrasi faktor-faktor psikologis dan teknologi. Belum banyak penelitian sebelumnya yang secara komprehensif mengkaji keterkaitan antara AR, PR, TR, dan PI terhadap niat perilaku dalam konteks filantropi Islam berbasis digital, khususnya pada kelompok generasi ini. Variabel AR (diwakili oleh kondisi pendukung, pengaruh sosial, kemudahan penggunaan, dan kebermanfaatan), PR (meliputi risiko keamanan dan privasi), PI, TR, dan BI dianalisis menggunakan pendekatan SEM dengan bantuan alat analisis Smart PLS. Hasil penelitian menunjukkan bahwa PR berpengaruh signifikan terhadap TR dan BI. Selain itu, AR dipengaruhi secara signifikan oleh PI. Sedangkan AR, PI dan TR tidak berpengaruh signifikan terhadap BI. Temuan ini bersifat spesifik pada Generasi Z. Oleh karena itu, penelitian selanjutnya disarankan untuk melakukan perbandingan antar generasi
Deteksi Suasana Hati Karyawan Berbasis Deep Learning Menggunakan CNN
Get PDFEmosi adalah bagian penting dari kehidupan manusia yang membantu kita memahami diri sendiri dan mengekspresikan perasaan. Emosi mencerminkan respons atau reaksi alami pada berbagai situasi yang dihadapi. Penelitian ini bertujuan untuk mengembangkan Model Convolutional Neural Network (CNN) yang mampu mengenali emosi manusia berdasarkan ekspresi wajah, khususnya pada kategori “Senang”, “Sedih”, “Marah” dan “Netral” dengan fokus pada karyawan setelah bekerja, untuk melihat apakah karyawan menikmati pekerjaannya atau tidak. Dengan menggunakan dataset berisi 2.059 gambar dari platform Kaggle, proses penelitian mencakup tahapan pengumpulan data, pre-processing data, pelatihan data hingga klasifikasi. Model ini dilatih selama 200 epoch dan menghasilkan akurasi sebesar 89,11% dengan performa yang cukup baik untuk kategori emosi “Senang” dan “Marah”. Namun, model masih mengalami kesultan dalam mengenali emosi “Sedih” dan “Netral”, kemungkinan karena kurangnya data pelatihan dan fitur yang belum optimal. Selama pelatihan, akurasi pada training menunjukkan peningkatan yang konsisten, sedangkan validasi sempat fluktuatif sebelum stabil. Analisis hasil pengujian menunjukkan bahwa model mampu memprediksi emosi dengan probabilitas tinggi, meskipun terdapat kendala dalam generalisasi ke kondisi yang lebih kompleks. Grafik dan evaluasi metriks,seperti precision, recall dan f1-score, menunjukkan adanya ruang untuk perbaikan, terutama dalam pengenalan emosi dengan nilai recall yang rendah. Penelitian ini memiliki potensi signifikan dalam pengenalan emosi melalui ekspresi wajah, yang digunakan untuk memahami emosi yang dialami oleh karyawan. Penelitian ini juga diperlukan pengembangan lebih lanjut guna meningkatkan kemampuan model dalam menangani komplesitas data
Application of YOLOv8 Model for Early Detection of Diseases in Bean Leaves
Get PDFBean plant is one of the high economic value horticultural commodities widely cultivated in Indonesia. However, its productivity declines due to pest attacks and leaf diseases. Farmers' limitations in accurately identifying disease types also pose obstacles in early mitigation efforts. Therefore, technology-based solutions capable of quickly and accurately detecting plant diseases are needed. This research aims to develop and evaluate the performance of a leaf disease detection model for bean plants using the You Only Look Once version 8 (YOLOv8) algorithm with a transfer learning approach. The dataset used consists of 1,037 images of bean leaves, classified into three categories: angular leaf spots, leaf rust, and healthy leaves. Data were obtained from two sources, namely field documentation in Sindang Village, Sukabumi Regency, and an open repository on GitHub. The dataset was divided into training data (70%), validation (20%), and testing (10%). The model was trained using the YOLOv8s architecture for 30 epochs and achieved a detection accuracy of 85%. Performance evaluation was conducted using precision, recall, and mean average precision (mAP) metrics. The results of this study are expected to be an initial contribution to the application of artificial intelligence in agriculture, particularly in helping farmers efficiently detect leaf diseases in beans to improve productivity and quality of harvest
Optimizing Artificial Intelligence-Based Waste Bank Management
No full textThis study examines the implementation of artificial intelligence (AI) technology to optimize waste bank management in West Pamulang, Indonesia. With the national waste volume reaching 68.5 million tons in 2023 and an annual growth rate of 2-4%, sustainable waste management presents critical challenges. West Pamulang accounts for 60% of regional waste, while Indonesia's 8,000 waste banks only reach 1.7% of the contribution to national waste reduction. Using a mixed method approach, the study was conducted in five waste banks in West Pamulang, South Tangerang during January-April 2025, involving 45 participants selected through purposive sampling. Data collection included participatory observations, interviews, questionnaires, and documentation studies. Reliability was assessed using Cronbach's Alpha 0.89, with validity guaranteed through triangulation. Ethical safeguards include informed consent, data anonymization, and institutional ethical approval. The results show significant operational improvements through AI technologies: computer vision-based classification systems, real-time transaction recording, educational chatbots, and volume prediction systems. Quantitative analysis revealed an increase in transaction efficiency by 75%, a 60% decrease in classification errors, and a decrease in data management time from day to minute. The AI predictive model achieves 92% accuracy in volume estimation and 15% fuel savings through route optimization. The classification system shows an accuracy of 89-97%, reducing the sorting time by 70%. Implementation challenges include limited digital literacy, infrastructure gaps, and inadequate policy support. The study recommends training programs, cost-effective platforms, and multi-stakeholder collaboration for a sustainable AI-enhanced waste management system.This study examines the implementation of artificial intelligence (AI) technology to optimize waste bank management in West Pamulang, Indonesia. With the national waste volume reaching 68.5 million tons in 2023 and an annual growth rate of 2-4%, sustainable waste management presents critical challenges. West Pamulang accounts for 60% of regional waste, while Indonesia's 8,000 waste banks only reach 1.7% of the contribution to national waste reduction. Using a mixed method approach, the study was conducted in five waste banks in West Pamulang, South Tangerang during January-April 2025, involving 45 participants selected through purposive sampling. Data collection included participatory observations, interviews, questionnaires, and documentation studies. Reliability was assessed using Cronbach's Alpha 0.89, with validity guaranteed through triangulation. Ethical safeguards include informed consent, data anonymization, and institutional ethical approval. The results show significant operational improvements through AI technologies: computer vision-based classification systems, real-time transaction recording, educational chatbots, and volume prediction systems. Quantitative analysis revealed an increase in transaction efficiency by 75%, a 60% decrease in classification errors, and a decrease in data management time from day to minute. The AI predictive model achieves 92% accuracy in volume estimation and 15% fuel savings through route optimization. The classification system shows an accuracy of 89-97%, reducing the sorting time by 70%. Implementation challenges include limited digital literacy, infrastructure gaps, and inadequate policy support. The study recommends training programs, cost-effective platforms, and multi-stakeholder collaboration for a sustainable AI-enhanced waste management system
Development of a Web-Based Reservation System to Improve the Efficiency of Catering Services
Get PDFDigital transformation has become essential for improving service efficiency across various sectors, including micro, small, and medium enterprises (MSMEs) in the catering industry. Manual reservation processes often cause service delays, inaccurate data recording, and decreased customer satisfaction. This study aims to develop a web-based catering reservation system to automate bookings, streamline service management, and improve interactions between service providers and customers. The system was developed using the Agile Software Development methodology with the Scrum framework, which supports iterative, adaptive development and active user involvement. A User-Centered Design (UCD) approach was also adopted to ensure usability and alignment with user needs. The development process involved requirement analysis, system and interface design using UML, implementation with Laravel and MySQL, and evaluation through Black Box Testing and User Acceptance Testing (UAT). The system provides core features such as menu ordering, transaction management, payment confirmation, and report generation, all presented in a responsive and accessible interface. Results show that the system improves service efficiency and enhances user satisfaction, particularly in terms of system navigation, clarity of information, and ease of use. However, the study has limitations, including its application within a single MSME environment and the absence of advanced features like automated notifications or integrated digital payments. These contextual constraints limit the generalizability of findings. In conclusion, the developed system contributes to the digitalization of catering services and offers promising potential for broader application. Future research should explore scalability across various MSME types and enhance system capabilities to meet wider operational demands.Digital transformation has become essential for improving service efficiency across various sectors, including micro, small, and medium enterprises (MSMEs) in the catering industry. Manual reservation processes often cause service delays, inaccurate data recording, and decreased customer satisfaction. This study aims to develop a web-based catering reservation system to automate bookings, streamline service management, and improve interactions between service providers and customers. The system was developed using the Agile Software Development methodology with the Scrum framework, which supports iterative, adaptive development and active user involvement. A User-Centered Design (UCD) approach was also adopted to ensure usability and alignment with user needs. The development process involved requirement analysis, system and interface design using UML, implementation with Laravel and MySQL, and evaluation through Black Box Testing and User Acceptance Testing (UAT). The system provides core features such as menu ordering, transaction management, payment confirmation, and report generation, all presented in a responsive and accessible interface. Results show that the system improves service efficiency and enhances user satisfaction, particularly in terms of system navigation, clarity of information, and ease of use. However, the study has limitations, including its application within a single MSME environment and the absence of advanced features like automated notifications or integrated digital payments. These contextual constraints limit the generalizability of findings. In conclusion, the developed system contributes to the digitalization of catering services and offers promising potential for broader application. Future research should explore scalability across various MSME types and enhance system capabilities to meet wider operational demands
Decision Support System for Selecting Santri Organization Leaders Using AHP and TOPSIS
Get PDFThe election of the head of the student organization plays a vital role in the leadership regeneration process within Islamic boarding schools (pesantren). However, this process is often conducted manually and subjectively, leading to potential bias, limited transparency, and a lack of standardized documentation. To address this issue, this study aims to develop a web-based Decision Support System (DSS) that integrates the Analytical Hierarchy Process (AHP) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to facilitate a fair, measurable, and value-aligned selection process. The selection criteria were identified through interviews and observations, covering five main aspects: morality, discipline, leadership, academics, and socialization. AHP was applied to calculate the priority weights of the criteria, while TOPSIS was used to rank five alternative candidates based on their proximity to the ideal leader profile. The results show that morality emerged as the highest-weighted criterion (0.4672), reflecting the pesantren’s emphasis on personal integrity. Candidate Ziyan Kamil achieved the top preference score of 0.754, indicating the closest alignment with the ideal candidate profile. System functionality was validated using black-box testing, confirming successful implementation of all core features. In conclusion, the developed DSS supports a more transparent, objective, and accountable selection process. It contributes not only to digital transformation in Islamic educational institutions but also serves as a replicable model for participatory and value-based governance in other pesantren environments
Implementation of IOT-based Motorcycle Security System with Cut Off Engine and Mobile Application
Get PDFThe increasing rate of motorcycle theft highlights the limitations of conventional security systems such as mechanical alarms and double locks, which often fail to provide proactive protection. This research proposes an Internet of Things (IoT)-based motorcycle security system integrating an engine cut-off feature, GPS tracking, vibration detection, and real-time notifications via a custom mobile application. Unlike previous solutions that commonly rely on SMS gateways or third-party services, this system leverages the Wemos D1 Mini microcontroller and Firebase Realtime Database to enable high-speed, two-way communication between the vehicle and the user. The system allows real-time vehicle location monitoring, remote engine control, and immediate detection of suspicious activities through the SW-420 vibration sensor connected to an audible buzzer alarm. The Android application, developed independently using the Kodular platform, not only provides digital vehicle location mapping but also enables quick engine deactivation and emergency alerts within seconds. Laboratory and field tests confirm a response time of less than one second for the engine cut-off function and accurate GPS tracking with an average deviation of under 10 meters. The primary innovation of this study lies in the full integration of IoT components, mobile interfaces, and cloud databases into a single platform without external dependencies, thereby enhancing efficiency, reliability, and system flexibility. The results demonstrate the system’s potential to deliver adaptive and modular vehicle security solutions, with opportunities for future enhancements such as geofencing, biometric authentication, and hybrid connectivity for improved resilience
Peran Belanja Hedonis, Live Streaming, dan Diskon dalam Mendorong Pembelian Impulsif di Tiktok Shop
Get PDFTiktok Shop menjadi salah satu fitur dari platform Tiktok yang dapat dimanfaatkan untuk melakukan pemasaran produk secara online sebagai akibat dari perkembangan teknologi. Tujuan penelitian ini adalah agar mengetahui pengaruh belanja hedonis, live streaming, dan diskon pada Tiktok Shop secara parsial dan simultan terhadap pembelian impulsif. Sebuah metode penelitian kuantitatif digunakan dengan mengumpulkan data dari 100 responden pengguna Tiktok Shop. Digunakan program SPSS versi 25 untuk menganalisis data. Uji yang digunakan adalah uji t, uji f, uji koefisien determinasi, uji normalitas, uji multikolinearitas, uji heterokedastisitas, uji regresi linear berganda. Hasil pengujian parsial menunjukan bahwa belanja hedonis dan diskon memiliki pengaruh positif terhadap pembelian impulsif tetapi live streaming memiliki pengaruh negatif terhadap pembelian impulsif. Hasil uji f menunjukan bahwa belanja hedonis, live streaming, dan diskon secara bersamaan memiliki pengaruh terhadap pembelian impulsif. Hasil penelitian ini membuka wawasan baru mengenai bagaimana emosi dan kebutuhan psikologis menciptakan mengalaman berbelanja yang menyenangkan serta menerapkan strategi diskon dapat memengaruhi keputusan pembelian impulsif. Sedangkan live streaming tidak begitu bisa menjadi faktor yang mempengaruhi pembelian impulsif. Pelaku usaha harus dapat merancang strategi pemasaran lebih efektif dengan menggabungkan faktor-faktor hedonis, seperti pengalaman belanja yang menyenangkan melalui live streaming dan strategi diskon agar dapat meningkatkan pembelian impulsif konsumen