ANALISIS DAMPAK EKONOMI PEDAGANG KAKI LIMA DI KOTA BOGOR DENGAN PENDEKATAN INPUT OUTPUT ANALYSIS

ABSTRACTA very high jobless people in the city has created an informal sector. This informal sector has become an alternative way for them to find a job. It has happened since it is very relatively easy joining or leaving this informal sector as there is not any particular regulation required. This research aims to study the contribution of street stalls upon the multiplier output, revenue, employment and forward-backward linkage over the economy. Method of analysis has been using input-output analysis and the research location has taken place in Bogor. The result of the research has indicated that the availability of the street stalls in the city cannot be considered as a marginal sector as they have been contributing positively the city economy either in the multiplier analysis or forward-backward linkage. Organizing and registering the street stalls in Bogor should have to be executed properly and periodically in order to find out their economical potential, making sure that they will not jeopardize the landscape of public facility. However, a particular association should have to be established in order to ease the supervision of the street stalls and to improve their quality accordingly.Keyword : Street stalls, informal sector, output, revenue, manpowerABSTRAKTingginya tingkat pengangguran perkotaan menumbuhkan sektor informal. Pedagang kaki lima merupakan salah satu sektor informal menjadi alternatif bagi mereka yang tidak mendapatkan pekerjaan disebabkan mudahnya untuk masuk dan keluar di sektor informal relatif mudah karena tidak ada aturan secara khusus yang mensyaratkan. Penelitian ini bertujuan untuk mengetahui kontribusi pedagang kaki lima terhadap multiplier output, pendapatan, penyerapan tenaga kerja dan forward-backward linkage terhadap perekonomian. Metode analisis yang digunakan adalah analisis input-output dan lokasi penelitian di Kota Bogor. Hasil penelitian menunjukan keberadaan pedagang kaki lima perkotaan tidak dapat dipandang sebagai sektor yang marginal, pedagang kaki lima memberikan kontribusi positif terhadap perekonomian perkotaan baik dalam analisis multiplier dan analisis forward-backward linkage. Penataan dan pendataan pedagang kaki lima di Kota Bogor perlu dilakukan secara periodik agar dapat diketahui potensi ekonomi, tidak mengganggu landscap dan peruntukan fasilitas umum kota. Perlunya pembentukan assosiasi agar dapat lebih mudah dalam pengawasan dan upaya peningkatan kualitas pedagang kaki lima.Kata Kunci : Pedagang Kaki Lima, Sektor Informal, Output, Pendapatan, Tenaga Kerj

Strategi Pengembangan Koperasi Melalui Kolaborasi dan Transformasi Digital di Kota Bogor

The research aims to identify the influential factors in developing cooperative development strategies in the city of Bogor. Research data using primary data and analysis using quantitative methods with SWOT and AHP analysis. The results of the research on the internal-external (IE) matrix show that the position of cooperative development is in cell one by implementing a growth strategy through vertical integration, by means of backward integration, meaning that in order to develop cooperatives in Bogor City, they must be independent, which means the use of all factors of production rely on the cooperative's own business, both products, human resources and other resources. The results of AHP show that the most prioritized strategy implemented is a strategy of trying to improve information and technology systems so that they can innovate and compete with other similar businesses. Cooperatives can make efforts to increase cooperation with the government and the private sector in the field of science and technology development so that cooperatives can innovate and compete in the market, as well as efforts to increase public trust in cooperatives and the need to make regulations and policies that support the development of cooperatives

Image Restoration Effect on DCT High Frequency Removal and Wiener Algorithm for Detecting Facial Key Points

This study aims to figure out the effect of using Histogram Equalization and Discrete Cosine Transform (DCT) in detecting facial keypoints, which can be applied for 3D facial reconstruction in face recognition. Four combinations of methods comprising of Histogram Equalization, removing low-frequency coefficients using Discrete Cosine Transform (DCT) and using five feature detectors, namely: SURF, Minimum Eigenvalue, Harris-Stephens, FAST, and BRISK were used for test. Data that were used for test were obtained from Head Pose Image and ORL Databases. The result from the test were evaluated using F-score. The highest F-score for Head Pose Image Dataset is 0.140 and achieved through the combination of DCT & Histogram Equalization with feature detector SURF. The highest F-score for ORL Database is 0.33 and achieved through the combination of DCT & Histogram Equalization with feature detector BRISK

The Effect of Using Histogram Equalization and Discrete Cosine Transform on Facial Keypoint Detection

This study aims to figure out the effect of using Histogram Equalization and Discrete Cosine Transform (DCT) in detecting facial keypoints, which can be applied for 3D facial reconstruction in face recognition. Four combinations of methods comprising of Histogram Equalization, removing low-frequency coefficients using Discrete Cosine Transform (DCT) and using five feature detectors, namely: SURF, Minimum Eigenvalue, Harris-Stephens, FAST, and BRISK were used for test. Data that were used for test were obtained from Head Pose Image and ORL Databases. The result from the test were evaluated using F-score. The highest F-score for Head Pose Image Dataset is 0.140 and achieved through the combination of DCT & Histogram Equalization with feature detector SURF. The highest F-score for ORL Database is 0.33 and achieved through the combination of DCT & Histogram Equalization with feature detector BRISK

Sistem Identifikasi Titik Kritis Halal Menggunakan Algoritma Forward Chaining

Halal products are obligatory to be used by people who are Muslim. When viewed in terms of the number of the Muslim population in the world and Indonesia, halal products have very potential economic opportunities. However, halal products have the risk of becoming non-halal if the accompanying process and storage do not follow halal rules. Therefore, it is necessary to identify the critical halal point, the point where the potential for such change occurs. So far, identification is made manually, of course there will be opportunities for identification errors to happen and it will take a relatively long time. To overcome these problems, identification can use a computer-based system. Forward chaining is an algorithm that is suitable for identifying halal points, because in SJH LPPOM MUI there is a decision tree for identifying halal critical points which is carried out in the same forward sequence as the forward chaining algorithm process flow. The development of a halal critical point identification system is carried out using the Software Development Life Cycle V-model method, the PHP programming language and the MySQL Database Management System. The system was successfully tested using Whitebox testing, including unit testing, integration testing, and overall system testing. Then testing using Blackbox testing techniques by comparing the results of identifying critical points using the system with the results of identifying critical points manually producing the same results. User satisfaction testing was also carried out using the End User Computing Satisfaction method and obtained an average satisfaction score of 86.53%Keywords – halal products, critical halal point, AI, forward chainin

The Effect of Environmental Management Accounting (EMA) on Financial Performance and Working Capital Management (WCM) as Mediating Variables

Sustainable accounting is a concern where demands on financial information become wider. The research aims to analyze the effect of EMA on the company's financial performance and working capital management (WCM) as a mediator. The data was obtained through a questionnaire survey of seventy eight respondents from textile manufacturing companies in Bogor Regency. The path analysis model is tested using Smart PLS. The results of the study found that EMA has a direct significant effect on financial performance and the role of working capital management as a mediator contributes to the indirect effect of EMA on financial performance. EMA information is critical in providing information that increases managers

The Effect of Using Histogram Equalization and Discrete Cosine Transform on Facial Keypoint Detection

This study aims to figure out the effect of using Histogram Equalization and Discrete Cosine Transform (DCT) in detecting facial keypoints, which can be applied for 3D facial reconstruction in face recognition. Four combinations of methods comprising of Histogram Equalization, removing low-frequency coefficients using Discrete Cosine Transform (DCT) and using five feature detectors, namely: SURF, Minimum Eigenvalue, Harris-Stephens, FAST, and BRISK were used for test. Data that were used for test were obtained from Head Pose Image and ORL Databases. The result from the test were evaluated using F-score. The highest F-score for Head Pose Image Dataset is 0.140 and achieved through the combination of DCT & Histogram Equalization with feature detector SURF. The highest F-score for ORL Database is 0.33 and achieved through the combination of DCT & Histogram Equalization with feature detector BRISK

Image Restoration Effect on DCT High Frequency Removal and Wiener Algorithm for Detecting Facial Key Points

This study aims to figure out the effect of using Histogram Equalization and Discrete Cosine Transform (DCT) in detecting facial keypoints, which can be applied for 3D facial reconstruction in face recognition. Four combinations of methods comprising of Histogram Equalization, removing low-frequency coefficients using Discrete Cosine Transform (DCT) and using five feature detectors, namely: SURF, Minimum Eigenvalue, Harris-Stephens, FAST, and BRISK were used for test. Data that were used for test were obtained from Head Pose Image and ORL Databases. The result from the test were evaluated using F-score. The highest F-score for Head Pose Image Dataset is 0.140 and achieved through the combination of DCT & Histogram Equalization with feature detector SURF. The highest F-score for ORL Database is 0.33 and achieved through the combination of DCT & Histogram Equalization with feature detector BRISK