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"Nowadays the quality aspects of information technology (IT) implementation plays an imperative role in the case of supporting services to its related stakeholders, mainly more on the issue of an effective and efficient process. To provide a more reliable, understandable, reusable, and maintainable academic information systems, Graduate School of IPB needs to improve the quality of its IT. Development of information systems (IS) through the proper stages of analysis and design expected to create a reliable and qualified IS. This research will evaluate the effectiveness of Academic Management IS. An evaluation conducted by observing the current system, using questionnaires, and measurement using a Likert scale. Questionnaire instrument tested for its validity (Product Moment) and reliability (Cronbachs Alpha) using SPSS version 19. This study adopts an integrated database schema that IPB has developed and designing research seminar online registration prototypes using object-oriented approach for a better IS future development."

"ABSTRACTA multi level and multi node supply chain multi agent simulation model is established based on the two maximum inventory level replenishment strategies, which consider the superior shipments. The effect of these two replenishment strategies on the bullwhip effect of supply chain role level and enterprise node is discussed. This paper describes in detail how the supply chain enterprises deal with important issues such as, receipt from superior, subordinate delivery, and order to the higher level. What is more important is that how to carry out demand forecasting and replenishment using the maximum inventory level strategy when ordering to the superiors. Results show that the bullwhip effects on the other three kinds of scenarios are all different from those with a higher quantity of goods, which the bullwhip effect is not notable. The bullwhip effect significantly increases from downstream to upstream. In the development of replenishment strategy, compared to not considering the superior shipments, considering it will significantly reduce the layer bullwhip effect and node bullwhip effect of the agents in supply chain. Considering the superior shipments has brought beneficial impact to the order quantity stability of each layer and the order quantity stability of each node enterprise."

"Kualitas pendidikan tinggi di Indonesia merupakan salah satu upaya penting dalam menghasilkan sumber daya manusia unggul yang dapat memberikan manfaat besar bagi negara. Salah satu aspek penilaian yang dapat dijadikan acuan adalah ketepatan waktu lulus mahasiswa serta perkembangan indeks prestasi yang sejalan. Mahasiswa dikatakan lulus tepat waktu jika menyelesaikan studi dalam kurun waktu empat tahun. Saat ini, penelitian terkait prediksi ketepatan waktu lulus mahasiswa di Indonesia masih terbatas, dengan penelitian terakhir hanya mencakup lingkup Universitas Indonesia. Penelitian ini bertujuan untuk membandingkan setidaknya lima model prediksi dan memanfaatkan ensemble learning untuk membangun model yang diinginkan. Metrik yang digunakan sebagai acuan adalah F1-Score, dengan hasil akhir model ensemble learning yang memanfaatkan stacking classifier mencapai nilai 83%. Produk akhir dari penelitian ini adalah sebuah website yang memiliki fitur prediksi dan fitur statistik. Fitur prediksi digunakan untuk memprediksi ketepatan waktu lulus berdasarkan model machine learning yang telah dikembangkan. Fitur statistik menyediakan berbagai visualisasi yang memberikan informasi terkait ketepatan waktu lulus pada tingkat nasional, universitas, dan program studi. Visualisasi yang digunakan mencakup line chart, pie chart, geo chart, dan bar chart, serta menyediakan data mentah untuk informasi yang lebih sederhana.

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The quality of higher education in Indonesia is a crucial effort in producing superior human resources that can significantly benefit the country. One of the assessment aspects that can be used as a reference is the timeliness of student graduation and the development of a corresponding performance index. Students are considered to graduate on time if they complete their studies within four years. Currently, research related to predicting the timeliness of student graduation in Indonesia is still limited, with the last study covering only the University of Indonesia. This study aims to compare at least five predictive models and utilize ensemble learning to build the desired model. The metric used as a reference is the F1-Score, with the final result of the ensemble learning model utilizing a stacking classifier reaching a score of 83%. The final product of this research is a website featuring both prediction and statistical tools. The prediction feature is used to predict the timeliness of graduation based on the previously developed machine learning model. The statistical feature provides various visualizations that offer information related to graduation timeliness at the national, university, and study program levels. The visualizations used include line charts, pie charts, geo charts, and bar charts, and also provide raw data for simpler information."

"Kualitas pendidikan tinggi di Indonesia merupakan salah satu upaya penting dalam menghasilkan sumber daya manusia unggul yang dapat memberikan manfaat besar bagi negara. Salah satu aspek penilaian yang dapat dijadikan acuan adalah ketepatan waktu lulus mahasiswa serta perkembangan indeks prestasi yang sejalan. Mahasiswa dikatakan lulus tepat waktu jika menyelesaikan studi dalam kurun waktu empat tahun. Saat ini, penelitian terkait prediksi ketepatan waktu lulus mahasiswa di Indonesia masih terbatas, dengan penelitian terakhir hanya mencakup lingkup Universitas Indonesia. Penelitian ini bertujuan untuk membandingkan setidaknya lima model prediksi dan memanfaatkan ensemble learning untuk membangun model yang diinginkan. Metrik yang digunakan sebagai acuan adalah F1-Score, dengan hasil akhir model ensemble learning yang memanfaatkan stacking classifier mencapai nilai 83%. Produk akhir dari penelitian ini adalah sebuah website yang memiliki fitur prediksi dan fitur statistik. Fitur prediksi digunakan untuk memprediksi ketepatan waktu lulus berdasarkan model machine learning yang telah dikembangkan. Fitur statistik menyediakan berbagai visualisasi yang memberikan informasi terkait ketepatan waktu lulus pada tingkat nasional, universitas, dan program studi. Visualisasi yang digunakan mencakup line chart, pie chart, geo chart, dan bar chart, serta menyediakan data mentah untuk informasi yang lebih sederhana.

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The quality of higher education in Indonesia is a crucial effort in producing superior human resources that can significantly benefit the country. One of the assessment aspects that can be used as a reference is the timeliness of student graduation and the development of a corresponding performance index. Students are considered to graduate on time if they complete their studies within four years. Currently, research related to predicting the timeliness of student graduation in Indonesia is still limited, with the last study covering only the University of Indonesia. This study aims to compare at least five predictive models and utilize ensemble learning to build the desired model. The metric used as a reference is the F1-Score, with the final result of the ensemble learning model utilizing a stacking classifier reaching a score of 83%. The final product of this research is a website featuring both prediction and statistical tools. The prediction feature is used to predict the timeliness of graduation based on the previously developed machine learning model. The statistical feature provides various visualizations that offer information related to graduation timeliness at the national, university, and study program levels. The visualizations used include line charts, pie charts, geo charts, and bar charts, and also provide raw data for simpler information."