Purpose: With rapid increase of Android devices and application systems, there is a strong demand for Android application programmers. A lot of schools are offering Android programming courses to meet this demand. However, Android programming can be different from the conventional one because it needs interactive functions through interfaces with users, which makes the study more difficult. This paper aims to propose an Android Programming Learning Assistance System, namely, APLAS, to assist the Java-based Android programming study and education. Design/methodology/approach: By adopting the test-driven development method, APLAS is designed to achieve independent learning without the presence of teachers. Using JUnit and Robolectric, the answers from the students are automatically marked in APLAS. To cover extensive materials in Android programming, APLAS offers four stages where each stage involves several topics. Findings: To evaluate the effectiveness of APLAS, we implemented the Unit Converter assignment that covers the first two topics, namely, Basic user interface in the first stage and basic activity in the second stage. Through solving the assignment, it is expected to learn basic application development. Forty novice students of an IT department in Indonesia were asked to solve both topics separately. Originality/value: The results show that APLAS is useful and helpful for the self-study of Android programming, as they could complete codes with good execution performances.

Nowadays, enhancing campus environments through mitigations of air pollutions is an essential endeavor to support academic achievements, health, and safety of students and staffs in higher educational institutes. In laboratories, pollutants from welding, auto repairs, or chemical experiments can drastically degrade the air quality in the campus, endangering the respiratory and cognitive health of students and staffs. Besides, in universities in Indonesia, automobile emissions of harmful substances such as carbon monoxide (CO), nitrogen dioxide (NO₂), and hydrocarbon (HC) have been a serious problem for a long time. Almost everybody is using a motorbike or a car every day in daily life, while the number of students is continuously increasing. However, people in many campuses including managements do not be aware these problems, since air quality is not monitored. In this paper, we present a real-time air quality monitoring system utilizing Internet of Things (IoT) integrated sensors capable of detecting pollutants and measuring environmental conditions to visualize them. By transmitting data to the SEMAR IoT application server platform via an ESP32 microcontroller, this system provides instant alerts through a web application and Telegram notifications when pollutant levels exceed safe thresholds. For evaluations of the proposed system, we adopted three sensors to measure the levels of CO, NO₂, and HC and conducted experiments in three sites, namely, Mechatronics Laboratory, Power and Emission Laboratory, and Parking Lot, at the State Polytechnic of Malang, Indonesia. Then, the results reveal Good, Unhealthy, and Dangerous for them, respectively, among the five categories defined by the Indonesian government. The system highlighted its ability to monitor air quality fluctuations, trigger warnings of hazardous conditions, and inform the campus community. The correlation of the sensor levels can identify the relationship of each pollutant, which provides insight into the characteristics of pollutants in a particular scenario.

Analisis sentimen atau opinion mining merupakan topik riset dari cabang penelitian text mining. Fokus dari analisis sentimen adalah melakukan analisis opini dari suatu dokumen teks, sehingga membantu usaha untuk melakukan riset pasar atas opini publik. Data opini diperoleh dari jejaring sosial Twitter dalam Bahasa Indonesia dengan topik suatu pantai.Klasifikasi opini diperlukan untuk memudahkan pengguna dalam melihat opini positif, negatif, ataupun netral. Algoritma yang digunakan dalam klasifikasi adalah Support Vector Machine. Pada penelitian digunakan dataset dari 10 pantai yang ada di Indonesia sebanyak 500 tweet. Hasil akurasi dari klasifikasi menggunakan algoritma Support Vector Machine sebesar 74,39%. Selanjutnya data opini dari kuesioner ditambahkan untuk mengelompokkan pantai berdasarkan ketersediaan sumber daya, fasilitas, akses, kesiapan masyarakat, potensi pasar dan posisi pariwisata. Dalam proses pengelompokan data ini digunakan metode K-Means.

Database course is the main courses in the department of informatics and computer science. This course aims to provide knowledge to students to build and manage data on Database Management Systems (DBMS) like MySQL. Therefore, the implementation of database processing practices in one of the DBMS such as MySQL is very important as basic skills in the field of informatics. With the growing number of informatics students, lectures cannot correct the results of student exercises quickly. For that, we need a system that can help lecturers to make corrections of query exercise on MySQL automatically, which is called Online Judge MySQL. This application was developed using NodeJS to execute queries that users input and ReactJS to build its interfaces. Testing is done by using this application for the online exam in several classes simultaneously. The results show that this application can correct the test results quickly and lightly.